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## Small LLMs Are Weak Tool Learners: A Multi-LLM Agent Weizhou Shen 1 , Chenliang Li 2 , Hongzhan Chen 1 , Ming Yan 2 * , Xiaojun Quan 1 ∗ , Hehong Chen 2 , Ji Zhang 2 , Fei Huang 2 1 School of Computer Science and Engineering, Sun Yat-sen University, China 2 Alibaba Group shenwzh3@mail2.sysu.edu.cn, quanxj3@mail.sysu.edu.cn, ym119608@alibaba-inc.com https://github.com/X-PLUG/Multi-LLM-Agent ## Abstract Large Language Model (LLM) agents significantly extend the capabilities of standalone LLMs, empowering them to interact with external tools (e.g., APIs, functions) and complete various tasks in a self-directed fashion. The challenge of tool use demands that LLMs not only understand user queries and generate answers accurately but also excel in task planning, tool invocation, and result summarization. While traditional works focus on training a single LLM with all these capabilities, performance limitations become apparent, particularly with smaller models. To overcome these challenges, we propose a novel approach that decomposes the aforementioned capabilities into a planner, caller, and summarizer. Each component is implemented by a single LLM that focuses on a specific capability and collaborates with others to accomplish the task. This modular framework facilitates individual updates and the potential use of smaller LLMs for building each capability. To effectively train this framework, we introduce a two-stage training paradigm. First, we fine-tune a backbone LLM on the entire dataset without discriminating sub-tasks, providing the model with a comprehensive understanding of the task. Second, the fine-tuned LLM is used to instantiate the planner, caller, and summarizer respectively, which are continually fine-tuned on respective sub-tasks. Evaluation across various tool-use benchmarks illustrates that our proposed multi-LLM framework surpasses the traditional single-LLM approach, highlighting its efficacy and advantages in tool learning. ## 1 Introduction Large Language Models (LLMs) have revolutionized natural language processing with remarkable proficiency in understanding and generating text. Despite their impressive capabilities, LLMs are * Corresponding authors. Figure 1: A conceptual comparison of the traditional single-LLM agent framework (top) and the proposed multi-LLM agent framework, α -UMi (bottom). <details> <summary>Image 1 Details</summary>  ### Visual Description ## Diagram: Single LLM vs. Multi-Agent LLM System ### Overview The image presents a comparative diagram illustrating two different approaches to utilizing Large Language Models (LLMs): a single LLM and a multi-agent LLM system. The diagram highlights the flow of information and control in each approach, emphasizing the interaction between instructions, the LLM(s), and external tools. ### Components/Axes * **Top Section:** Represents the single LLM approach. * **Instruction:** Depicted as a speech bubble containing "Q" and "A," indicating a question-and-answer interaction. * **Single LLM:** A neural network icon labeled "Single LLM" with a light yellow background. * **Tools:** A box containing icons for RapidAPI, Python, OpenAI, and Java, labeled "Tools." * **Arrows:** Double-headed arrows indicate bidirectional communication between the instruction and the LLM, and between the LLM and the tools. * **Bottom Section:** Represents the multi-agent LLM system approach. * **Instruction:** Similar to the top section, depicting a question-and-answer interaction. * **Planner:** A neural network icon labeled "Planner" with a light green background. * **Caller:** A neural network icon labeled "Caller." * **Summarizer:** A neural network icon labeled "Summarizer." * **Tools:** Similar to the top section, containing icons for RapidAPI, Python, OpenAI, and Java, labeled "Tools." * **Arrows:** Double-headed arrows indicate bidirectional communication between the instruction and the Planner, and between the Planner and the tools. Blue arrows indicate "Guidance & control" from the Planner to the Caller and Summarizer. An orange arrow indicates "Feedback" from the Caller and Summarizer to the Planner. * **Legend:** Located at the bottom of the image. * Blue Arrow: "Guidance & control" * Orange Arrow: "Feedback" ### Detailed Analysis * **Single LLM Approach:** * An instruction is directly fed into a single LLM. * The single LLM interacts with external tools. * The interaction between the LLM and the tools is bidirectional. * **Multi-Agent LLM System Approach:** * An instruction is fed into a "Planner" LLM. * The "Planner" LLM coordinates the "Caller" and "Summarizer" LLMs. * The "Planner" LLM interacts with external tools. * The "Planner" provides guidance and control to the "Caller" and "Summarizer" (blue arrows). * The "Caller" and "Summarizer" provide feedback to the "Planner" (orange arrows). ### Key Observations * The single LLM approach is a direct interaction between the instruction, the LLM, and the tools. * The multi-agent LLM system approach introduces a "Planner" LLM to coordinate other LLMs ("Caller" and "Summarizer"). * The multi-agent system incorporates feedback loops between the "Planner" and the other LLMs. ### Interpretation The diagram illustrates two distinct architectures for utilizing LLMs. The single LLM approach represents a simpler, more direct method, where a single model handles both understanding the instruction and interacting with external tools. The multi-agent system, on the other hand, introduces a layer of orchestration with the "Planner," allowing for more complex task decomposition and coordination between specialized LLMs. The feedback loop in the multi-agent system suggests a mechanism for iterative refinement and improved performance. The choice between these architectures depends on the complexity of the task and the desired level of control and specialization. </details> not without limitations. Notably, they lack domain specificity, real-time information, and face challenges in solving specialized problems such as mathematics (Gou et al., 2023) and program compilation (OpenAI, 2023a). Hence, integrating LLMs with external tools, such as API calls and Python functions, becomes imperative to extend their capabilities and enhance the overall performance. Consequently, LLM agents have become a prominent area for both academia and industry, employing large language models to determine when and how to utilize external tools to tackle various tasks. In addition to exploring proprietary LLMs like GPT-4, researchers have also actively engaged in developing customizable agent systems by finetuning open-source LLMs on diverse tool-use datasets (Patil et al., 2023; Tang et al., 2023; Qin et al., 2023b; Gou et al., 2023). The challenge of tool learning demands sufficiently large and complex LLMs. These models must not only comprehend user queries but also excel in task planning, tool selection and invocation, and result summarization (Yujia et al., 2023). These capabilities draw upon different facets of the LLMs; for instance, planning relies more on reasoning ability, while tool selection and invocation demand legal and ac- curate request writing, and result summarization requires adept conclusion-drawing skills. While conventional approaches (Qin et al., 2023b; Gou et al., 2023; Zeng et al., 2023) focus on training a single open-source LLM with all these capabilities, notable performance limitations have been observed, especially with smaller open-source LLMs (Touvron et al., 2023a,b). Moreover, the tools could be updated frequently in practical scenarios, when the entire LLM requires potential retraining. To address these challenges, we propose a multiLLM agent framework for tool learning, α -UMi 1 . As illustrated in Figure 1, α -UMi decomposes the capabilities of a single LLM into three components, namely planner, caller, and summarizer. Each of these components is implemented by a single LLM and trained to focus on a specific capability. The planner is designed to generate the rationale based on the current state of the system and weighs between selecting the caller or summarizer to generate downstream output, or even deciding to terminate the execution. The caller is directed by the rationale and responsible for invocating specific tools. The summarizer is guided by the planner to craft the ultimate user answer based on the execution trajectory. These components collaborate seamlessly to accomplish various tasks. Compared to previous approaches, our modular framework has three distinct advantages. First, each component undergoes training for a designated role, ensuring enhanced performance for each capability. Second, the modular structure allows for individual updates to each component as required, ensuring adaptability and streamlined maintenance. Third, since each component focuses solely on a specific capability, potentially smaller LLMs can be employed. To effectively train this multi-LLM framework, we introduce a novel global-to-local progressive fine-tuning strategy (GLPFT). First, an LLM backbone is trained on the original training dataset without discriminating between sub-tasks, enhancing the comprehensive understanding of the toollearning task. Three copies of this LLM backbone are created to instantiate the planner, caller, and summarizer, respectively. In the second stage, the training dataset is reorganized into new datasets tailored to each LLM's role in tool use, and continual 1 In astronomy, the name ' α -UMi' is an alias of the Polaris Star ( https://en.wikipedia.org/wiki/Polaris ), which is actually a triple star system consisting of a brighter star (corresponding to the planner) and two fainter stars (corresponding to the caller and the summarizer). fine-tuning of the planner, caller, and summarizer is performed on their respective datasets. We employ LLaMA-2 (Touvron et al., 2023b) series to implement the LLM backbone and evaluate our α -UMi agent on several tool learning benchmarks (Qin et al., 2023b; Tang et al., 2023). Experimental results demonstrate that our proposed framework outperforms the single-LLM approach across various model and data sizes. Moreover, we show the necessity of the GLPFT strategy for the success of our framework and delve into the reasons behind the improved performance. Finally, the results confirm our assumption that smaller LLMs can be used in our multi-LLM framework to cultivate individual tool learning capabilities and attain a competitive overall performance. To sum up, this work makes three critical contributions. First, we demonstrate that small LLMs are weak tool learners and introduce α -UMi, a multiLLM framework for building LLM agents, that outperforms the existing single-LLM approach in tool use. Second, we propose a GLPFT fine-tuning strategy, which has proven to be essential for the success of our framework. Third, we perform a thorough analysis, delving into data scaling laws and investigating the underlying reasons behind the superior performance of our framework. ## 2 Related Works ## 2.1 Tool Learning The ability of LLMs to use external tools has become a pivotal component in the development of AI agents, attracting rapidly growing attention (Qin et al., 2023b; Schick et al., 2023; Yang et al., 2023b; Shen et al., 2023; Patil et al., 2023; Qin et al., 2023a). Toolformer (Schick et al., 2023) was one of the pioneering work in tool learning, employing tools in a self-supervised manner. Subsequently, a diverse array of external tools has been employed to enhance LLMs in various ways, including the improvement of real-time factual knowledge (Yang et al., 2023a; Nakano et al., 2021), multimodal comprehension and generation (Yang et al., 2023b; Wu et al., 2023a; Yang et al., 2023c), code and math reasoning (Gou et al., 2023; OpenAI, 2023a), and domain knowledge of specific AI models and APIs (Shen et al., 2023; Li et al., 2023; Qin et al., 2023b). Different from previous approaches relying on a single LLM for tool learning, we introduce a novel multi-LLM collaborated tool learning framework designed for smaller open-source Figure 2: An illustration of how α -UMi works to complete a task. <details> <summary>Image 2 Details</summary>  ### Visual Description ## Flow Diagram: Multi-Agent Task Execution ### Overview The image is a flow diagram illustrating a multi-agent system designed for task execution. The system involves a Planner, Caller, and Summarizer, each represented as a neural network. The diagram outlines the flow of information and actions between these agents, guided by prompts and decision points. ### Components/Axes * **Agents:** Planner, Caller, Summarizer (each depicted as a neural network) * **Inputs:** Instruction, Planner prompt, Caller prompt, Summarizer prompt, Tool call & Observation * **Outputs:** Rationale r_t, Action a_t, Final answer, Give up * **Decision Points:** "Next: Caller?", "Next: Summarizer?" (diamond shapes) * **Flow Direction:** Arrows indicate the direction of information and control flow. * **Icons:** * Person icon with speech bubble: Represents the initial instruction. * Computer screen icons: Represent prompts for each agent (Planner, Caller, Summarizer). * Speech bubble icons: Represent Rationale, Action, and Final answer. * Gear icon on a computer screen: Represents Tool call & Observation. * Red "X" icon: Represents giving up. ### Detailed Analysis or ### Content Details 1. **Instruction:** The process begins with an "Instruction" given to the "Planner". 2. **Planner:** * Receives an "Instruction" and a "Planner prompt". * Outputs a "Rationale r_t". * Receives "Tool call & Observation" as feedback. 3. **Caller:** * Receives a "Caller prompt" and the "Rationale r_t". * Outputs an "Action a_t". 4. **Decision Point 1: "Next: Caller?"** * If "Yes", the flow returns to the "Caller" with "Tool call & Observation". * If "No", the flow proceeds to the next decision point. 5. **Decision Point 2: "Next: Summarizer?"** * If "Yes", the flow proceeds to the "Summarizer". * If "No", the process ends with "Give up". 6. **Summarizer:** * Receives a "Summarizer prompt". * Outputs a "Final answer". ### Key Observations * The system uses a cyclical process involving the Planner and Caller, potentially iterating multiple times based on the "Next: Caller?" decision. * The Summarizer is invoked only after the Caller process is deemed complete. * The system has a "Give up" exit point if neither the Caller nor the Summarizer is deemed the next step. ### Interpretation The diagram illustrates a multi-agent system designed to solve tasks through iterative planning, action, and summarization. The Planner generates a rationale, the Caller executes an action based on that rationale, and the system decides whether to iterate on the action or summarize the results. The decision points ("Next: Caller?", "Next: Summarizer?") suggest a mechanism for adaptive task execution, where the system can dynamically adjust its strategy based on the intermediate results. The "Tool call & Observation" feedback loop allows the Planner to refine its rationale based on the outcomes of the Caller's actions. The "Give up" exit point indicates a mechanism for handling tasks that cannot be solved within the defined framework. </details> LLMs. This framework decomposes the comprehensive abilities of LLMs into distinct roles, namely a planner, caller, and summarizer. ## 2.2 LLM-powered Agents Leveraging the capabilities of LLMs such as ChatGPT (OpenAI, 2022) and GPT-4 (OpenAI, 2023b), AI agent systems have found application in diverse scenarios. For instance, solutions like BabyAGI (Nakajima, 2023) and AutoGPT (Gravitas, 2023) have been developed to address daily problems, while Voyager (Wang et al., 2023) and Ghost (Zhu et al., 2023) engage in free exploration within Minecraft games. Additionally, MetaGPT (Hong et al., 2023), ChatDev (Qian et al., 2023), and AutoGen (Wu et al., 2023b) contribute to the development of multi-agent frameworks tailored for software development and problem-solving. Various techniques have been proposed to augment agent capabilities from different perspectives. The chain-of-thought series (Wei et al., 2022; Wang et al., 2022; Yao et al., 2022, 2023) and Reflextion (Shinn et al., 2023) contribute to enhancing agents' reasoning abilities, while MemoryBank (Zhong et al., 2023) enriches agents with long-term memory. Recent efforts have also emerged in fine-tuning open-source LLMs as agents, exemplified by works like FiREACT (Chen et al., 2023) and AgentTuning (Zeng et al., 2023). However, these endeavors primarily focus on finetuning a single LLM, unlike our approach, which explores an effective method for fine-tuning a multiLLM agent specifically for tool learning. ## 3 Methodology ## 3.1 Preliminary Agents for tool learning are systems designed to assist users in completing tasks through a series of decision-making processes and tool use (Yujia et al., 2023). In recent years, these agents commonly adhere to the ReACT framework (Yao et al., 2022). The backbone of the agent is an LLM denoted as M . Given the user instruction q and the system prompt P , the agent solves the instruction step by step. In the t th step, the LLM M generates a rationale r t and an action a t based on the instruction and the current state of the system: $$r _ { t } , a _ { t } = \mathcal { M } ( \mathcal { P } , \tau _ { t - 1 } , q ) , \quad ( 1 )$$ where τ t -1 = { r 1 , a 1 , o 1 , ..., r t -1 , a t -1 , o t -1 } denotes the previous execution trajectory. Here, o t denotes the observation returned by tools when the action a t is supplied. In the final step of the interaction, the agent generates rationale r n indicating that the instruction q is solved along with the final answer a n or that it will abandon this execution run. Therefore, no observation is included in this step. ## 3.2 The α -UMi Framework As previously mentioned, the task of tool learning imposes a significant demand on the capabilities of LLMs, including task planning, tool invocation, and result summarization. Coping with all these capabilities using a single open-source LLM, especially when opting for a smaller LLM, appears to be challenging. To address this challenge, we introduce the α -UMi framework, which breaks down the tool learning task into three sub-tasks and assigns each sub-task to a dedicated LLM. Figure 1 presents an illustration of our framework, which incorporates three distinct LLM components: planner M plan, caller M call, and summarizer M sum. These components are differentiated by their roles in tool use, and each component model has a unique task definition, system prompt 2 , and model input. 2 The prompts for each LLM are provided in Appendix A. The workflow of α -UMi is shown in Figure 2. Upon receiving the user instruction q , the planner generates a rationale comprising hints for the this step. This may trigger the caller to engage with the tools and subsequently receive observations from the tools. This iterative planner-caller-tool loop continues until the planner determines that it has gathered sufficient information to resolve the instruction. At this point, the planner transitions to the summarizer to generate the final answer. Alternatively, if the planner deems the instruction unsolvable, it may abandon the execution. Planner : The planner assumes responsibility for planning and decision-making, serving as the 'brain' of our agent framework. Specifically, the model input for the planner comprises the system prompt P plan, the user instruction q , and the previous execution trajectory τ t -1 . Using this input, the planner generates the rationale r t : $$r _ { t } = \mathcal { M } _ { p l a n } ( \mathcal { P } _ { p l a n } , \tau _ { t - 1 } , q ) . \quad ( 2 ) \quad o f \, c o r d$$ Following the rationale, the planner generates the decision for the next step: (1) If the decision is 'Next: Caller', the caller will be activated and an action will be generated for calling tools. (2) If the decision is 'Next: Summarizer', the summarizer will be activated to generate the final answer for the user, and the agent execution will finish. (3) If the decision is 'Next: Give up', it means that the user's instruction cannot be solved in the current situation, and the system will be terminated. Caller: Interacting with the tools requires the LLM to generate legal and useful requests, which may conflict with other abilities such as reasoning and general response generation during fine-tuning. Therefore, we train a specialized caller to generate the action for using tools. The caller takes the user instruction q and the previous execution trajectory τ t -1 as input. To make the caller focus on the planner's rationale r t in the current step, we also design a prompt P call to explicitly remind the caller: $$a _ { t } = \mathcal { M } _ { c a l l } ( \mathcal { P } _ { c a l l } , \tau _ { t - 1 } , q , r _ { t } ) . \quad ( 3 ) \quad m a n$$ Summarizer: The agent's final response, which aims to offer informative and helpful information to the user, is distinct from the rationales that primarily focus on planning and reasoning. Therefore, we employ a dedicated summarizer tasked with generating the final answer a n . This model utilizes a concise prompt P sum designed to guide the model Figure 3: Global-to-local progressive fine-tuning. <details> <summary>Image 3 Details</summary>  ### Visual Description ## Diagram: LLM Fine-tuning Process ### Overview The image illustrates a two-stage fine-tuning process for a Large Language Model (LLM). The process involves global fine-tuning of a pre-trained LLM, followed by local fine-tuning of subtasks. ### Components/Axes * **Top Region (Global Fine-tuning):** * Label: "Global Fine-tuning" on the left. * Component: "Pre-trained LLM" with a llama icon above it. * Component: "Single-LLM" represented as a neural network diagram. * Text Box: Contains the following text: * "Thought: ..." * "Action: ..." * "Action input: ..." * "Answer: ..." * **Bottom Region (Subtasks & Local Fine-tuning):** * Label: "Subtasks & Local Fine-tuning" on the left. * Component: "Planner" represented as a neural network diagram. * Text Box: Contains the following text: * "...<msk><msk>..." * "Thought: ..." * "Next: ..." * "<msk><msk>..." * Component: "Caller" represented as a neural network diagram. * Text Box: Contains the following text: * "...<msk><msk>..." * "Action: ..." * "Action input: ..." * "<msk><msk>..." * Component: "Summarizer" represented as a neural network diagram. * Text Box: Contains the following text: * "...<msk><msk>..." * "Final Answer: ..." ### Detailed Analysis * **Global Fine-tuning Stage:** * The "Pre-trained LLM" (with the llama icon) feeds into the "Single-LLM". * The output of the "Single-LLM" is represented by a black line going to each of the subtasks. * The "Pre-trained LLM" outputs to a text box containing "Thought", "Action", "Action input", and "Answer". * **Subtasks & Local Fine-tuning Stage:** * The "Planner", "Caller", and "Summarizer" are individual neural networks. * Each subtask receives input from the "Single-LLM" (black line). * The "Planner" has a text box containing "Thought" and "Next". * The "Caller" has a text box containing "Action" and "Action input". * The "Summarizer" has a text box containing "Final Answer". * The "Planner", "Caller", and "Summarizer" also receive input from the "Pre-trained LLM" (red line). ### Key Observations * The diagram illustrates a hierarchical approach to fine-tuning an LLM. * The global fine-tuning stage prepares the LLM for specific subtasks. * The local fine-tuning stage optimizes the LLM for each subtask. * The use of "..." indicates that the text boxes contain incomplete information or placeholders. * The "<msk><msk>" tags suggest masked language modeling or a similar technique. ### Interpretation The diagram presents a method for improving the performance of LLMs on complex tasks by breaking them down into smaller, more manageable subtasks. The global fine-tuning stage likely aims to adapt the pre-trained LLM to the general domain of the task, while the local fine-tuning stage focuses on optimizing the LLM for each specific subtask. The use of masked language modeling suggests that the fine-tuning process involves predicting missing words or phrases in the input text, which can help the LLM learn to better understand and generate text. The diagram highlights the importance of task decomposition and targeted fine-tuning for achieving optimal performance with LLMs. </details> in concentrating on summarizing the execution trajectory and presenting the answer to the user: $$a _ { n } = \mathcal { M } _ { s u m } ( \mathcal { P } _ { s u m } , \tau _ { n - 1 } , q , r _ { n } ) . \quad ( 4 )$$ In Figure 6 and Figure 7, we show several cases of our α -UMi on downstream tasks. ## 3.3 Global-to-Local Progressive Fine-Tuning To effectively fine-tune the above multi-LLM system is a complex endeavor: On one hand, generating the rationale, action, and final answer can facilitate each other during the training process, and enhance the model's comprehension of the entire agent task (Chen et al., 2023). On the other hand, the constraints on model capacity make it challenging to fine-tune a small LLM to achieve peak performance in generating rationales, actions, and final answers simultaneously (Dong et al., 2023). Taking into account these two points, we propose a global-to-local progressive fine-tuning (GLPFT) strategy for α -UMi. The motivation behind this strategy is to first exploit the mechanism by which the generation of rationale, action, and final answer can mutually enhance each other. Then, once the single LLM reaches its performance ceiling, it is subsequently split into planner, caller and summarizer for further fine-tuning, in order to enhance its capabilities in the subtasks and mitigate the performance constraints due to limited model capacity. As depicted in Figure 3, this GLPFT strategy comprises two distinct stages. The first stage involves global fine-tuning, where we fine-tune a backbone LLM on the original training dataset without distinguishing between sub-tasks. After this stage, the backbone LLM is trained to sequentially output the rationale, action, and answer as introduced in Section 3.1. Then, we create three copies of the backbone LLM, designated as the planner, caller, and summarizer, respectively. The second stage is local fine-tuning, where we reorganize the training dataset tailored to each LLM's role, as introduced in Section 3.2. We then proceed to fine-tune the planner, caller, and summarizer on their respective datasets, thereby further enhancing their specific abilities in each sub-task. During this local fine-tuning stage, we opt to reuse the set of user instructions curated in the global fine-tuning stage. The only adjustment made to the training set is the change in the format of the training data. As illustrated in Figure 3, the finetuning objective during the second stage for the planner, caller, and summarizer is oriented towards generating the rationale, action, and final answer, respectively. While the gradients from other text spans are stopped. Simultaneously, we refine the system prompts for the training data of the planner, caller, and summarizer, as detailed in Appendix A. ## 3.4 Discussions The proposed α -UMi framework and GLPFT strategy are founded on three main principles. Firstly, the limited ability and capacity of small LLMs, such as LLaMA-7B, pose challenges during finetuning in tool learning tasks. In contrast, α -UMi decomposes complex tasks into simpler ones, reducing the workload on LLMs. Secondly, α -UMi offers increased flexibility in prompt design, allowing us to create specific prompts and model inputs for each LLM to fully leverage its capabilities in sub-tasks. Thirdly, the GLPFT strategy bridges the gap between fine-tuning on the whole tool learning task and on each sub-task, leading to a more successful fine-tuning process of the multi-LLM system. In the following experimental sections, we will focus on demonstrating these principles. Recent studies have explored multi-agent systems based on LLMs across various domains, such as social communication (Park et al., 2023; Wei et al., 2023), software development (Qian et al., 2023; Hong et al., 2023), and problem solving (Wu et al., 2023b). These frameworks typically rely on powerful closed-source LLMs, demanding advanced capabilities such as automatic cooperation and feedback-abilities that extend beyond those of open-source small LLMs. In contrast, our α -UMi aims to ease the LLM's workload in tool-use tasks by integrating multiple LLMs to form an agent, particularly suitable for open-source, small LLMs. Additionally, we introduce the novel GLPFT method for fine-tuning the multi-LLM system, a contribution not found in existing multi-agent works. ## 4 Experimental Settings ## 4.1 Benchmarks We evaluate the effectiveness of our α -UMi on two tool learning benchmarks: ToolBench (Qin et al., 2023b) and ToolAlpaca (Tang et al., 2023). These tasks involve integrating API calls to accomplish tasks, where the agent must accurately select the appropriate API and compose necessary API requests. Moreover, we partition the test set of ToolBench into in-domain and out-of-domain based on whether the tools used in the test instances have been seen during training. This division allows us to evaluate performance in both in-distribution and out-of-distribution scenarios. For additional details and statistics regarding these datasets, please refer to Appendix B. We also evaluate α -UMi on other benchmarks such as program-aided agent for mathematical reasoning (Hendrycks et al., 2021; Cobbe et al., 2021). The results are shown in Appendix D. ## 4.2 Metrics The tasks in ToolBench involve calling APIs through RapidAPI 3 . This process frequently encounters problems such as API breakdowns, which impacts the fairness of the comparison. To address this problem, we introduce two types of evaluations for ToolBench. In Section 5.1, we first compare the output of agent with the annotated reference at each step 4 , which avoids real-time API callings. The metrics for this evaluation include Action EM (Act. EM), Argument F1 (Arg. F1), and Rouge-L (R-L) as proposed by Li et al. (2023). Moreover, we examine the frequency of API name hallucinations (Hallu.) and the accuracy (Plan ACC) of the agent's planning decisions at each step for using tools invocation, generating answer, or giving up. The reference annotations are based on verified ChatGPT execution results provided in ToolBench. We also provide the results based on real-time RapidAPI calling in Section 5.2, which is the original evaluation method used by the ToolBench team. For ToolAlpaca, we assess the process correctness rate (Proc.) and the final answer correctness rate (Ans.) (Tang et al., 2023), both by GPT-4. 3 https://rapidapi.com/hub. 4 Refer to Appendix C for more details of the evaluation. Table 1: Overall evaluation results on ToolBench and ToolAlpaca. 'ToolLLaMA (len = 4096)' and 'ToolLLaMA (len = 8192)' mean setting the max input length of ToolLLaMA to 4096 and 8192, respectively. | Model | | ToolBench (in-domain) | ToolBench (in-domain) | ToolBench (in-domain) | ToolBench (in-domain) | ToolBench (out-of-domain) | ToolBench (out-of-domain) | ToolBench (out-of-domain) | ToolBench (out-of-domain) | | ToolAlpaca | ToolAlpaca | |---------------------------------|------------------|-------------------------|-------------------------|-------------------------|-------------------------|-----------------------------|-----------------------------|-----------------------------|-----------------------------|------------------|------------------|------------------| | | Plan ACC | Act. EM | Hallu. | Arg. F1 | R-L | Plan ACC | Act. EM | Hallu. | Arg. F1 | R-L | Proc. | Ans. | | Close-SourceLLM | Close-SourceLLM | Close-SourceLLM | Close-SourceLLM | Close-SourceLLM | Close-SourceLLM | Close-SourceLLM | Close-SourceLLM | Close-SourceLLM | Close-SourceLLM | Close-SourceLLM | Close-SourceLLM | Close-SourceLLM | | ChatGPT (0-shot) GPT-4 (0-shot) | 83.33 80.28 | 58.67 55.52 | 7.40 5.98 | 45.61 48.74 | 23.08 28.69 | 81.62 77.80 | 54.67 55.26 | 8.19 5.12 | 40.08 47.45 | 22.85 30.61 | 33 41 | 37 44 | | Model Size = 7B | Model Size = 7B | Model Size = 7B | Model Size = 7B | Model Size = 7B | Model Size = 7B | Model Size = 7B | Model Size = 7B | Model Size = 7B | Model Size = 7B | Model Size = 7B | Model Size = 7B | Model Size = 7B | | ToolLLaMA (len = 4096) | 66.42 | 19.47 | 33.94 | 15.98 | 2.06 | 68.21 | 30.75 | 25.35 | 25.07 | 5.78 | - | - | | ToolLLaMA (len = 8192) | 77.02 | 47.56 | 4.03 | 42.00 | 15.26 | 77.76 | 45.07 | 3.45 | 40.41 | 18.10 | - | - | | Single-LLM | 81.92 | 53.26 | 2.32 | 45.57 | 42.66 | 84.61 | 56.54 | 2.26 | 50.09 | 47.99 | 11 | 11 | | Multi-LLM one-stage | 87.52 | 45.11 | 7.71 | 38.02 | 41.01 | 88.42 | 53.40 | 2.52 | 45.79 | 46.39 | 2 | 9 | | Single-LLM multi-task | 85.06 | 51.83 | 2.96 | 44.25 | 27.40 | 86.55 | 56.89 | 2.77 | 49.50 | 32.58 | 28 | 18 | | α -UMi w/o reuse | 88.24 | 55.50 | 0.53 | 48.97 | 39.98 | 87.91 | 58.02 | 2.32 | 50.55 | 42.59 | - | - | | α -UMi w/ reuse | 88.92 | 58.94 | 0.57 | 52.24 | 43.17 | 89.72 | 60.47 | 0.45 | 53.60 | 46.26 | 41 | 35 | | Model Size = 13B | Model Size = 13B | Model Size = 13B | Model Size = 13B | Model Size = 13B | Model Size = 13B | Model Size = 13B | Model Size = 13B | Model Size = 13B | Model Size = 13B | Model Size = 13B | Model Size = 13B | Model Size = 13B | | Single-LLM | 81.01 | 59.67 | 1.53 | 52.35 | 42.16 | 86.74 | 60.04 | 2.03 | 52.94 | 48.46 | 33 | 29 | | Multi-LLM one-stage | 86.49 | 50.54 | 5.11 | 41.96 | 36.21 | 87.45 | 56.71 | 3.23 | 47.49 | 41.62 | 22 | 19 | | Single-LLM multi-task | 86.36 | 58.96 | 2.00 | 49.28 | 28.41 | 86.64 | 62.78 | 3.42 | 53.29 | 35.46 | 28 | 16 | | α -UMi w/o reuse | 86.33 | 60.07 | 0.39 | 53.11 | 35.09 | 87.75 | 61.63 | 2.95 | 52.54 | 37.70 | - | - | | α -UMi w/ reuse | 87.87 | 63.03 | 0.37 | 57.65 | 43.46 | 88.73 | 64.21 | 0.24 | 57.38 | 42.50 | 41 | 35 | ## 4.3 Implementation Details We opt for LLaMA-2-chat-7B/13B (Touvron et al., 2023b) as the backbone to implement our framework. In the first stage of our GLPFT, we conduct fine-tuning for the backbone LLM with a learning rate of 5e-5 for 2 epochs. Then, we create three copies of this fine-tuned backbone to instantiate the planner, caller, and summarizer, respectively. In the second stage, we fine-tune the three LLMs with a reduced learning rate of 1e-5. The planner and caller undergo fine-tuning for 1 epoch, while the summarizer undergoes fine-tuning for 2 epochs. Weset the global batch size to 48 and employ DeepSpeed ZeRO Stage3 (Rajbhandari et al., 2021) to speed up the fine-tuning process. All experimental results are obtained using greedy decoding, with the maximum sequence length set at 4096. ## 4.4 Baselines We compare our method with three baseline methods, namely Single-LLM, Multi-LLMone-stage and Single-LLM multi-task . Single-LLM refers to the traditional single-LLM tool learning approach. Multi-LLMone-stage involves directly fine-tuning the planner, caller, and summarizer on their own sub-task datasets, without employing our two-stage fine-tuning strategy. Single-LLM multi-task refers to using the same LLM to fulfill the roles of planner, caller, and summarizer. This particular LLM is fine-tuned on a combined dataset comprising the three sub-task datasets and functions similarly to our multi-LLM framework. We also evaluate the performance of ChatGPT and GPT-4 with 0-shot setting, and ToolLLaMA (Qin et al., 2023b), which is a 7B LLaMA model fine-tuned on ToolBench. ## 5 Results and Analysis ## 5.1 Overall Results The main results are presented in Table 1. We elaborate on our observations from five perspectives: First, when compared to ChatGPT and ToolLLama, our α -UMi outperforms them on all metrics except for the answer correctness on ToolAlpaca. α -UMi exceeds these two baselines in terms of Plan ACCand R-L considerably, demonstrating its alignment with annotated reference in terms of planning execution steps and generating final answers. It is worth mentioning that ToolLLaMA only exhibits acceptable performance when the input length is 8192. At an input length of 4096, ToolLLaMA shows deterioration across various metrics, particularly exhibiting a very high hallucination rate. In contrast, α -UMi only requires the input length of 4096 to achieve a satisfying performance. We attribute this to our multi-LLM system design, which allows each small LLM to focus on its sub-task, thereby reducing the requirement for input length. Second, α -UMi outperforms the Single-LLM agent. On ToolBench, we unveil substantial improvements with α -UMi, particularly in Plan ACC, Act. EM, Hallu., and Arg. F1. On ToolAlpaca, α -UMi also surpasses Single-LLM with both 7B and 13B backbones. These findings not only confirm the effectiveness of α -UMi in enhancing the agent's planning and API calling capabilities but also suggest a notable decrease in hallucinations, which can significantly elevate user satisfaction. Third, when comparing the results of methods with different model sizes, we note that agents with a 13B backbone exhibit superior performance com- Table 2: Results of real-time evaluation on ToolBench. 'ReACT' and 'DFSDT' denote reasoning strategies used to construct agents, as detailed in Section 5.2. 'Win' measures the relative win rate of each agent compared to ChatGPT-ReACT ('Method'=ReACT, 'Model'=ChatGPT), which does not have an associated win rate. | Method | Model | I1-Inst. | I1-Inst. | I1-Tool | I1-Tool | I1-Cat. | I1-Cat. | I2-Inst. | I2-Inst. | I2-Cat. | I2-Cat. | I3-Inst. | I3-Inst. | Average | Average | |----------|--------------|------------|------------|-----------|-----------|-----------|-----------|------------|------------|-----------|-----------|------------|------------|-----------|-----------| | Method | Model | Pass | Win | Pass | Win | Pass | Win | Pass | Win | Pass | Win | Pass | Win | Pass | Win | | | Claude-2 | 5.5 | 31.0 | 3.5 | 27.8 | 5.5 | 33.8 | 6.0 | 35.0 | 6.0 | 31.5 | 14.0 | 47.5 | 6.8 | 34.4 | | | ChatGPT | 41.5 | - | 44.0 | - | 44.5 | - | 42.5 | - | 46.5 | - | 22.0 | - | 40.2 | - | | | ToolLLaMA | 25.0 | 45.0 | 29.0 | 42.0 | 33.0 | 47.5 | 30.5 | 50.8 | 31.5 | 41.8 | 25.0 | 55.0 | 29.0 | 47.0 | | | GPT-4 | 53.5 | 60.0 | 50.0 | 58.8 | 53.5 | 63.5 | 67.0 | 65.8 | 72.0 | 60.3 | 47.0 | 78.0 | 57.2 | 64.4 | | | Claude-2 | 20.5 | 38.0 | 31.0 | 44.3 | 18.5 | 43.3 | 17.0 | 36.8 | 20.5 | 33.5 | 28.0 | 65.0 | 43.1 | 43.5 | | | ChatGPT | 54.5 | 60.5 | 65.0 | 62.0 | 60.5 | 57.3 | 75.0 | 72.0 | 71.5 | 64.8 | 62.0 | 69.0 | 64.8 | 64.3 | | | ToolLLaMA | 57.0 | 55.0 | 61.0 | 55.3 | 62.0 | 54.5 | 77.0 | 68.5 | 77.0 | 58.0 | 66.0 | 69.0 | 60.7 | 60.0 | | | GPT-4 | 60.0 | 67.5 | 71.5 | 67.8 | 67.0 | 66.5 | 79.5 | 73.3 | 77.5 | 63.3 | 71.0 | 84.0 | 71.1 | 70.4 | | | α -UMi (7B) | 65.0 | 59.5 | 68.0 | 66.0 | 64.0 | 57.0 | 81.5 | 76.5 | 76.5 | 72.0 | 70.0 | 63.0 | 70.9 | 65.9 | | | α -UMi (13B) | 65.5 | 61.5 | 69.0 | 66.0 | 65.0 | 62.5 | 84.5 | 75.0 | 81.0 | 74.5 | 71.0 | 66.0 | 72.2 | 67.7 | pared to their 7B counterparts. This observation implies that the shift from a 7B to a 13B model results in a noteworthy improvement in tool utilization capabilities. Significantly, α -UMi with a 7B backbone even outperforms the Single-LLM baseline with a 13B LLM, confirming our earlier assumption that smaller LLMs can be utilized in our multi-LLM framework to develop each capability and achieve competitive overall performance. Fourth, α -UMi outperforms Multi-LLMone-stage and Single-LLM multi-task . Multi-LLMone-stage even exhibits suboptimal performance compared to the Single-LLM baseline in metrics assessing API calling abilities, such as Act. EM, Hallu., and Arg. F1. This finding highlights the limitations of Multi-LLMone-stage when training each LLM on individual sub-tasks, compromising the comprehensive understanding of the tool-use task. Moreover, the subpar performance of Single-LLM multi-task reveals a limitation associated with the capacity of 7B and 13B models. The limited model capacity hinders the agent from effectively fulfilling the roles of planner, caller, and summarizer simultaneously. In contrast, through the application of the GLPFT strategy, α -UMi successfully mitigates this limitation, showcasing its effectiveness in achieving comprehensive tool learning capabilities. Finally, α -UMi w/o reuse represents that instead of reusing the user instructions in the first fine-tuning stage of GLPFT, a new set of user instructions are employed for the second stage of GLPFT. This setup is inspired by Chung et al. (2022), which has demonstrated that increasing the diversity of user instructions during fine-tuning can improve the performance and generalizability of LLMs. However, as presented in Table 1 and visualized in Figure 4, despite the increased diversity of instructions compared to α -UMiw/ reuse, α -UMiw/o reuse does not outperform α -UMiw/ reuse. We attribute this unexpected result to the following explanation: Since the objectives of the two training stages are different, using distinct sets of user instructions, each with its unique distribution, may introduces a harmful inductive bias that solving one group of the instructions in single-LLM format while the other group in multi-LLM format. In contrast, through the reuse of user instructions, the impact of varying distributions from different sets is mitigated. ## 5.2 Real-Time Test on ToolBench To assess the performance of LLMs for solving real tasks via RapidAPI, we follow the ToolEval method (Qin et al., 2023b) proposed by the ToolBench team to conduct a real-time evaluation on the test set of ToolBench. The LLMs under consideration include Claude-2 (Anthropic, 2023), ChatGPT, GPT-4, and ToolLLaMA. We apply two reasoning strategies for these LLMs to construct tool learning agents: the ReACT method, as introduced in Section 3.1, and the Depth First Search-based Decision Tree (DFSDT) (Qin et al., 2023b), which empowers the agent to evaluate and select between different execution paths. Two metrics are included to measure these LLMs' performance: pass rate , which calculates the percentage of tasks successfully completed, and win rate , which compares the agent's solution path with that of the standard baseline, ChatGPT-ReACT. The above two metrics are assessed by a ChatGPT evaluator with carefully crafted criteria. The empirical results presented in Table 2 demonstrate that our α -UMi (7B) surpasses both ChatGPT and ToolLLaMA by significant margins in terms of pass rate (+6.1 and +10.2, respectively) and win rate (+1.6 and +5.9, respectively). Figure 4: Results of data scaling law study on ToolBench with different evaluation metrics: (a) Plan ACC, (b) Act. EM, (c) Hallu, (d) Arg. F1, and (e) R-L. We randomly sampled five training sets with the scales of 12.1k, 15.7k, 31.3k, 47.0k, and 62.7k instances, accounting for 19.2%, 25%, 50%, 75%, and 100% of the training set, respectively. <details> <summary>Image 4 Details</summary>  ### Visual Description ## Line Charts: Performance Comparison of Different LLM Configurations ### Overview The image presents a series of five line charts comparing the performance of different Large Language Model (LLM) configurations across various metrics. The x-axis represents the data size (in thousands), and the y-axis represents the performance score for each metric. The charts compare "Single-LLM", "Multi-LLMone-stage", "Single-LLMmulti-task", "α-UMIw/o reuse", and "α-UMIw/ reuse". ### Components/Axes * **X-axis (all charts):** Data size in thousands, labeled with values 12.1k, 31.3k, 47.0k, and 62.7k. * **Y-axis (Plan ACC):** Performance score ranging from 80.0 to 87.5. * **Y-axis (Act. EM):** Performance score ranging from 50 to 60. * **Y-axis (Hallu.):** Performance score ranging from 0 to 8. * **Y-axis (Aug. F1):** Performance score ranging from 40 to 50. * **Y-axis (R-L):** Performance score ranging from 25 to 45. * **Chart Titles:** (a) Plan ACC, (b) Act. EM, (c) Hallu., (d) Aug. F1, (e) R-L. * **Legend (bottom-right):** * Blue line with circle markers: Single-LLM * Dark red dashed line with square markers: Multi-LLMone-stage * Green dashed line with plus markers: Single-LLMmulti-task * Light red dashed line with no markers: α-UMIw/o reuse * Red line with triangle markers: α-UMIw/ reuse ### Detailed Analysis #### (a) Plan ACC (Planning Accuracy) * **Single-LLM (Blue):** Starts at approximately 79 at 12.1k, increases to ~84 at 31.3k, then remains relatively stable around 84 until 62.7k. * **Multi-LLMone-stage (Dark Red):** Starts at ~83 at 12.1k, increases to ~87 at 31.3k, then decreases slightly to ~87 at 62.7k. * **Single-LLMmulti-task (Green):** Starts at ~83 at 12.1k, increases to ~86 at 31.3k, then remains relatively stable around 86 until 62.7k. * **α-UMIw/o reuse (Light Red):** Starts at ~83 at 12.1k, increases to ~87 at 31.3k, then remains relatively stable around 87 until 62.7k. * **α-UMIw/ reuse (Red):** Starts at ~84 at 12.1k, increases to ~87 at 31.3k, then increases to ~88 at 62.7k. #### (b) Act. EM (Action Exact Match) * **Single-LLM (Blue):** Starts at ~51 at 12.1k, increases to ~54 at 31.3k, then decreases to ~52 at 47.0k, and increases to ~54 at 62.7k. * **Multi-LLMone-stage (Dark Red):** Starts at ~47 at 12.1k, increases to ~52 at 31.3k, then decreases to ~52 at 47.0k, and decreases to ~48 at 62.7k. * **Single-LLMmulti-task (Green):** Starts at ~50 at 12.1k, increases to ~50 at 31.3k, then remains relatively stable around 50 until 62.7k. * **α-UMIw/o reuse (Light Red):** Starts at ~50 at 12.1k, increases to ~57 at 31.3k, then decreases to ~57 at 47.0k, and decreases to ~57 at 62.7k. * **α-UMIw/ reuse (Red):** Starts at ~54 at 12.1k, increases to ~58 at 31.3k, then increases to ~58 at 47.0k, and increases to ~59 at 62.7k. #### (c) Hallu. (Hallucination) * **Single-LLM (Blue):** Starts at ~2 at 12.1k, increases to ~3 at 31.3k, then decreases to ~1 at 47.0k, and increases to ~2 at 62.7k. * **Multi-LLMone-stage (Dark Red):** Starts at ~5 at 12.1k, decreases to ~1 at 31.3k, then increases to ~2 at 47.0k, and increases to ~6 at 62.7k. * **Single-LLMmulti-task (Green):** Starts at ~4 at 12.1k, increases to ~8 at 31.3k, then decreases to ~5 at 47.0k, and decreases to ~3 at 62.7k. * **α-UMIw/o reuse (Light Red):** Starts at ~4 at 12.1k, decreases to ~1 at 31.3k, then remains relatively stable around 1 until 62.7k. * **α-UMIw/ reuse (Red):** Starts at ~1 at 12.1k, increases to ~1 at 31.3k, then remains relatively stable around 1 until 62.7k. #### (d) Aug. F1 (Augmented F1 Score) * **Single-LLM (Blue):** Starts at ~36 at 12.1k, increases to ~46 at 31.3k, then remains relatively stable around 46 until 62.7k. * **Multi-LLMone-stage (Dark Red):** Starts at ~40 at 12.1k, increases to ~42 at 31.3k, then remains relatively stable around 42 until 62.7k. * **Single-LLMmulti-task (Green):** Starts at ~44 at 12.1k, decreases to ~42 at 31.3k, then remains relatively stable around 42 until 62.7k. * **α-UMIw/o reuse (Light Red):** Starts at ~43 at 12.1k, increases to ~43 at 31.3k, then remains relatively stable around 43 until 62.7k. * **α-UMIw/ reuse (Red):** Starts at ~47 at 12.1k, increases to ~51 at 31.3k, then remains relatively stable around 51 until 62.7k. #### (e) R-L (Reward Learning) * **Single-LLM (Blue):** Starts at ~36 at 12.1k, increases to ~43 at 31.3k, then increases to ~44 at 47.0k, and increases to ~45 at 62.7k. * **Multi-LLMone-stage (Dark Red):** Starts at ~40 at 12.1k, increases to ~42 at 31.3k, then increases to ~42 at 47.0k, and increases to ~44 at 62.7k. * **Single-LLMmulti-task (Green):** Starts at ~25 at 12.1k, increases to ~33 at 31.3k, then increases to ~37 at 47.0k, and decreases to ~30 at 62.7k. * **α-UMIw/o reuse (Light Red):** Starts at ~33 at 12.1k, increases to ~42 at 31.3k, then increases to ~42 at 47.0k, and increases to ~43 at 62.7k. * **α-UMIw/ reuse (Red):** Starts at ~40 at 12.1k, increases to ~43 at 31.3k, then increases to ~43 at 47.0k, and increases to ~45 at 62.7k. ### Key Observations * **α-UMIw/ reuse (Red):** Generally performs well across all metrics, often achieving the highest scores, especially as the data size increases. * **Single-LLMmulti-task (Green):** Shows variable performance, sometimes performing well (e.g., Plan ACC) and sometimes underperforming (e.g., R-L). * **Hallucination (c):** The α-UMIw/ reuse (Red) consistently shows the lowest hallucination rates. ### Interpretation The charts provide a comparative analysis of different LLM configurations, highlighting the impact of various architectural choices and training strategies on performance across different metrics. The "α-UMIw/ reuse" configuration appears to be a strong performer, particularly in terms of planning accuracy, action exact match, and minimizing hallucination. The performance variations across metrics suggest that different configurations are better suited for specific tasks or evaluation criteria. The increase in data size generally leads to improved performance for most configurations, indicating the importance of data scale in LLM training. The "Hallu." chart is particularly important, as it indicates the model's tendency to generate nonsensical or factually incorrect information. Lower scores on this metric are desirable. </details> While α -UMi underperforms GPT-4 in win rate , it exhibits pass rates on par with GPT-4 or even exceeds it in certain test groups such as I1-Inst. and I2-Inst. . Combining the findings from Section 5.1 and this section, we note that our multi-LLM agent outperforms several established baselines across diverse metrics on ToolBench, validating its efficacy. ## 5.3 Data Scaling Law To assess the impact of the amount of training data on performance, we conduct a data scaling law analysis with the 7B backbone on ToolBench, varying the number of annotated training instances from 12.1k to 62.7k. The results in different metrics are depicted in Figure 4. Several observations can be drawn from the results. Firstly, when comparing α -UMi (solid red curves) to Single-LLM (solid blue curves), there are significant and consistent enhancements in metrics such as Plan ACC, Act. EM, Hallu., and Arg. F1 across various scales of training data. While only minor improvements are observed in the R-L metric, which directly reflects the performance of the summarizer, this suggests that the performance enhancement of our framework is mainly attributed to the separation of the planner and the caller. Secondly, the performances of Multi-LLMone-stage and Single-LLM multi-task exhibit severe fluctuations in all metrics except for Plan ACC, indicating instability in training the framework through direct fine-tuning or multi-task fine-tuning. Thirdly, Single-LLM achieves opti- Figure 5: Curves of training loss. <details> <summary>Image 5 Details</summary>  ### Visual Description ## Chart: Loss vs. Epoch for Different Models ### Overview The image is a line graph showing the loss (y-axis) versus the number of epochs (x-axis) for different models. The models are "Single-LLM" and "α-UMi", and the loss is plotted for "Rationale", "Action", and "Answer" for each model. ### Components/Axes * **X-axis:** "# Epoch" ranging from 0 to 3. * **Y-axis:** "Loss" ranging from 0.0 to 0.5. * **Legend (top-right):** * Green: Rationale (Single-LLM) * Orange: Action (Single-LLM) * Light Blue: Answer (Single-LLM) * Dark Green: Rationale (α-UMi) * Teal: Action (α-UMi) * Brown: Answer (α-UMi) * A vertical dashed line is present at Epoch = 2. ### Detailed Analysis * **Rationale (Single-LLM) - Green:** Starts at approximately 0.52 loss and decreases rapidly until epoch 1, then decreases more slowly to approximately 0.17 at epoch 3. * **Action (Single-LLM) - Orange:** Starts at approximately 0.24 loss and decreases to approximately 0.04 at epoch 3. * **Answer (Single-LLM) - Light Blue:** Starts at approximately 0.18 loss and decreases to approximately 0.02 at epoch 3. * **Rationale (α-UMi) - Dark Green:** Starts at approximately 0.32 loss and decreases rapidly until epoch 1, then decreases more slowly to approximately 0.08 at epoch 3. * **Action (α-UMi) - Teal:** Starts at approximately 0.12 loss and decreases to approximately 0.01 at epoch 3. * **Answer (α-UMi) - Brown:** Starts at approximately 0.20 loss and decreases to approximately 0.03 at epoch 3. ### Key Observations * All loss curves decrease as the number of epochs increases, indicating learning. * The "Single-LLM" model's "Rationale" loss starts higher and decreases more slowly than the other curves. * The vertical dashed line at epoch 2 seems to indicate a change in the training process, as there is a slight change in the slope of some of the curves at this point. * The "Action" and "Answer" losses for both models are generally lower than the "Rationale" losses. * The α-UMi model generally has lower loss values than the Single-LLM model for the "Rationale" component. ### Interpretation The graph compares the training loss of two different models (Single-LLM and α-UMi) across three components: Rationale, Action, and Answer. The decreasing loss values indicate that both models are learning effectively during training. The α-UMi model appears to perform better than the Single-LLM model, especially in terms of the "Rationale" component, as it achieves lower loss values. The vertical line at epoch 2 suggests a potential change in the training regime, which may have influenced the learning rate or optimization process. The lower loss values for "Action" and "Answer" compared to "Rationale" might indicate that these components are easier to learn or are better optimized within the models. </details> mal results in different metrics at different data scales. For example, it attains peak performance in Plan ACC with 31.3k instances and the best Arg. F1 and R-L with 62.7k instances. This suggests the challenge of obtaining a single LLM that uniformly performs well across all metrics. In contrast, the performance of our framework consistently improves with increased data scale across all metrics. ## 5.4 Why α -UMi Works? We track the training process of our α -UMi approach to examine what makes it different from the Single-LLM baseline. To further investigate how each capability of the model evolves during training, we track the training loss on the rationale, action, and answer components of target responses. The results are depicted in Figure 5. As introduced in Section 4.3, α -UMi employs GLPFT and deviates from Single-LLM after two training epochs. Table 3: The cost of training and inference. | Model | Storage | Flops | Train Time | GPU Mem. | Infer. Time (Per Inst.) | |-------------------|------------------|-----------------------------|------------------|------------------|---------------------------| | Model Size = 7B | Model Size = 7B | Model Size = 7B | Model Size = 7B | Model Size = 7B | Model Size = 7B | | Single-LLM α -UMi | 7B 7B*3 | 4 . 8 ∗ 10 15 6 . 2 ∗ 10 15 | 41.54h 63.34h | 206G 206G | 6.41s 6.27s | | Model Size = 13B | Model Size = 13B | Model Size = 13B | Model Size = 13B | Model Size = 13B | Model Size = 13B | | Single-LLM α -UMi | 13B 13B*3 | 7 . 2 ∗ 10 15 9 . 7 ∗ 10 15 | 89.56h 129.96h | 308G 308G | 11.91s 11.09s | Therefore, our discussion focuses on the training curves of α -UMi from the third epoch. The plotted curves reveal a consistent decrease in the training loss for rationale, action, and answer components during the initial two epochs. However, in the third epoch, the losses of Single-LLM exhibit a nearly stagnant trend. In contrast, α -UMi experiences continued reductions in the losses associated with rationale and action, indicating further optimization within our α -UMi framework. These observations suggest that the key factor contributing to the success of α -UMi lies in its ability to surpass the performance upper-bound of Single-LLM. This is achieved by leveraging GLPFT and decomposing the agent into a multiLLM system, even after Single-LLM has attained its upper-bound abilities via sufficient fine-tuning. ## 5.5 Cost of α -UMi Given that α -UMi operates as a multi-LLM framework, it introduces potential additional costs in terms of training, storage, and deployment. Table 3 provides a summary of the costs associated with Single-LLM and α -UMi, based on execution logs on 8 Nvidia A100 GPUs with a 40G capacity. Our observations are threefold. Firstly, owing to its composition of a planner, a caller, and a summarizer, α -UMi demands three times the storage capacity compared to the Single-LLM framework, assuming they employ backbones of the same size. Secondly, the training of α -UMi requires 1.3 times the computational resources and 1.5 times the training duration compared to Single-LLM, while the GPU memory cost for training remains consistent between the two methods. Thirdly, during inference, the time required for both Single-LLM and α -UMi is similar, as we only distribute sub-tasks (rationale, action, and answer) to the three LLMs, without forcing them to generate extra contents, thus bringing nearly no extra cost when inference. Note that based on the findings presented in Table 1, α -UMi with a 7B backbone can outperform Single-LLM with a 13B backbone. Furthermore, the cost associated with α -UMi featuring a 7B model is lower than that of Single-LLM featuring a 13B model, both in terms of training and inference. This underscores the cost-effectiveness of α -UMi as a means to achieve, and even surpass, the performance of a larger LLM. ## 5.6 Case Study Figure 6 and Figure 7 show two cases of our α -UMi executing real tasks in ToolBench. In the case of Figure 6, the user specifies the available tools in the instructions, making the tool invocation process simpler. The α -UMi framework completes the task within two steps through the collaboration of the planner, caller, and summarizer. In the case of Figure 7, α -UMi initially attempts to use the 'video\_for\_simple\_youtube\_search' to obtain detailed video information at step 0. However, it realizes that this API has broken and cannot be invoked. Therefore, the planner informs the caller to try an alternative API and obtain accurate information. Ultimately, the user's task is successfully resolved. To further analyze the specific advantages of our α -UMi and Single-LLM frameworks in task execution, we have presented some comparative examples of the two frameworks in Tables 5, 6, 7, and 8. Tables 5 and 6 illustrate simple tasks that require only a single step tool invocation to be completed, in which case both α -UMi and Single-LLM can successfully accomplish the tasks. However, in the complex tasks presented in Tables 7 and 8, where the tasks require the models to accomplish some composite objectives, α -UMi's planner can quickly understand the user's intentions and plan out steps based on the prompts provided by the caller and summarizer. On the other hand, Single-LLM exhibited some behaviors that did not align with the user's intentions during planning, such as invoking APIs that did not match the intent and entering loops in these misaligned APIs, ultimately failing to provide sufficient information to complete the user's instructions. This result indicates that α -UMi's decomposing Single-LLM into a planner, caller, and summarizer reduces the burden on the model during reasoning, allowing the planner model to focus solely on understanding the user's intentions and making effective plans, thereby better accomplishing the tasks. ## 6 Conclusion The objective of this paper is to address the challenge of designing and fine-tuning a single small LLM to acquire the extensive abilities required for a tool learning agent. To this end, we introduce α -UMi, a multi-LLM tool learning agent framework that breaks down the tool learning task into three distinct sub-tasks delegated to three small LLMs: planner, caller, and summarizer. Moreover, we propose a global-to-local progressive fine-tuning strategy and demonstrate its effectiveness in training the multi-LLM framework. We evaluate our approach against single-LLM baselines on four tool learning benchmarks, supplemented by various indepth analyses, including a data scaling law experiment. Our findings highlight the significance of our proposed method, validating that the system's design for decomposing tool learning tasks and the progressive fine-tuning strategy contribute to enhancing the upper-bound ability of a single LLM. Besides, we acknowledge the potential to utilize small LLMs to surpass the capabilities of an agent framework that relies on a single, larger LLM. ## 7 Limitations While our framework has been demonstrated to outperform the single-LLM framework in tool learning tasks, there are still some limitations to this work. Firstly, there are additional avenues for exploration, such as integrating small LLMs with a powerful closed-source LLM like GPT-4 to create a 'large + small' collaborative multi-LLM tool learning agent. 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Memorybank: Enhancing large language models with long-term memory. arXiv preprint arXiv:2305.10250 . Xizhou Zhu, Yuntao Chen, Hao Tian, Chenxin Tao, Weijie Su, Chenyu Yang, Gao Huang, Bin Li, Lewei Lu, Xiaogang Wang, et al. 2023. Ghost in the minecraft: Generally capable agents for open-world enviroments via large language models with text-based knowledge and memory. arXiv preprint arXiv:2305.17144 . ## A System prompts ## A.1 P plan for ToolBench and ToolAlpaca You have assess to the following apis: {doc} The conversation history is: {history} You are the assistant to plan what to do next and whether is caller's or conclusion's turn to answer. Answer with a following format: The thought of the next step, followed by Next: caller or conclusion or give up. ## A.2 P call for ToolBench and ToolAlpaca You have assess to the following apis: {doc} The conversation history is: {history} The thought of this step is: {thought} Base on the thought make an api call in the following format: Action: the name of api that should be called in this step, should be exactly in {tool\_names}, Action Input: the api call request. ## A.3 P sum for ToolBench and ToolAlpaca Make a conclusion based on the conversation history: {history} ## A.4 P plan for MATH and GSM8K Solve the math problem step by step by integrating step-by-step reasoning and Python code, The problem is: {instruction} The historical execution logs are: {history} You are the assistant to plan what to do next, and shooce caller to generate code or conclusion to answer the problem. Answer with a following format: The thought of the next step, followed by Next: caller or conclusion. ## A.5 P call for MATH and GSM8K The problem is: {instruction} The historical execution logs are: {history} The thought of this step is: {thought} generate the code for this step ## A.6 P sum for MATH and GSM8K The problem is: {instruction} The historical execution logs are: {history} Make a conclusion based on the conversation history ## B Details of Benchmarks ## B.1 ToolBench ToolBench (Qin et al., 2023b) is a benchmark for evaluating an agent's ability to call APIs. The ToolBench team collects 16,464 real-world APIs from RapidAPI and a total of 125,387 execution trajectories as the training corpus. We randomly sample 62,694 execution trajectories as the training set, and the average number of execution steps is 4.1. The test set of ToolBench is divided into 6 groups, namely I1-instruction, I1-tool, I1-category, I2-instruction, I2-category, and I3-instruction. The groups whose name ends with 'instruction' means the test instructions in these groups use the tools in the training set, which is the in-domain test data. Otherwise, the groups whose name ends with 'tool' or 'category' means the test instructions do not use the tools in the training set, which is the outof-domain test data. Each group contains 100 user instructions, therefore the total in-domain test set contains 400 instructions, while the out-of-domain test set contains 200 instructions. The original evaluation metrics in ToolBench are the pass rate and win rate judged by ChatGPT. However, as introduced in Section 4.2, the APIs in RapidAPI update every day, which can cause network block, API breakdown, and exhausted quota. Therefore, to make a relatively fair comparison, we adopt the idea of Modelscope-Agent (Li et al., 2023) to compare the predictions of our model with the annotated GPT-4 outputs on the step level. Specifically, for the t th step, we input the model with the previous trajectory of GPT-4, ask our framework to generate the rationale and action of this step, and then compare the generated rationale and action of this step with the output of GPT-4. ## B.2 ToolAlpaca ToolAlpaca is another benchmark for evaluating API calling. Unlike ToolBench, the APIs and API calling results in ToolAlpaca are mocked from ChatGPT by imitating how the real APIs work. The Table 4: Overall results on MATH and GSM8K. | Model | MATH GSM8K ACC | MATH GSM8K ACC | |-----------------------|------------------|------------------| | Model Size = 7B | Model Size = 7B | Model Size = 7B | | Single-LLM | 17.38 | 37.90 | | Multi-LLM one-stage | 15.46 | 38.96 | | Single-LLM multi-task | 14.18 | 27.97 | | α -UMi | 25.60 | 49.73 | | Model Size = 13B | Model Size = 13B | Model Size = 13B | | Single-LLM | 20.26 | 44.88 | | Multi-LLM one-stage | 20.32 | 44.57 | | Single-LLM multi-task | 15.34 | 34.79 | | α -UMi | 28.54 | 54.20 | total number of training instances in ToolAlpaca is 4098, with an average of 2.66 execution steps per instance. The test set of ToolAlpaca contains 100 user instructions. The evaluation of ToolAlpaca is carried out by a simulator where the agent solves the instruction with the tools mocked by ChatGPT. Finally, GPT-4 judges if the execution process of the agent is consistent with the reference process pre-generated by ChatGPT (Proc. correctness) and whether the final answer generated by the agent can solve the user instruction (Ans. correctness). ## C Static Evaluation on ToolBench The evaluation method for ToolBench introduced in Section 4.2 is a static approach that assesses the output of the agent at each step individually. Specifically, for each step t , given the ground-truth annotation of the previous execution trajectory τ ∗ <t , the agent generates the rationale ˆ r t and action ˆ a t for this step: $$\hat { r } _ { t } , \hat { a } _ { t } = A g e n t ( \tau _ { < t } ^ { * } ) . \quad ( 5 )$$ Then, metrics are computed by comparing the generated ˆ r t and ˆ a t with the annotated ground-truth rationale r ∗ t and action a ∗ t for this step: $$M e t r i c = E v a l u a t e ( \hat { r } _ { t } , \hat { a } _ { t } , r _ { t } ^ { * } , a _ { t } ^ { * } ) . \quad ( 6 )$$ The advantage of this evaluation method is as follows. At each step, the agent only needs to take the previous ground-truth trajectory as input and outputs the current step's rationale and action. This prevents error propagation due to factors such as network blocks, API breakdowns, and exhausted quotas in any particular step, which could affect the fairness of comparison. This evaluation method is an effective complement to real-time evaluation. ## D α -UMi on Other Benchmarks ## D.1 MATH and GSM8K The MATH (Hendrycks et al., 2021) and GSM8K (Cobbe et al., 2021) benchmarks are originally designed to test the mathematical reasoning ability of LLMs. Following ToRA (Gou et al., 2023), we employ a program-aided agent to solve the mathematical problems presented in these datasets. In our scenario, the planner will generate certain rationales and comments to guide the generation of program, the caller will generate program to conduct mathematical calculation, and finally the summarizer will conclude the final answer. Since ToRA has not released its training data, to facilitate the training of our framework, we utilize gpt-3.5-turbo-1106 (OpenAI, 2022) and gpt-4 (OpenAI, 2023b) to collect execution trajectories in the training set of MATH and GSM8K and filter out the trajectories that do not lead to the correct final answer. Finally, we collect 5536 trajectories from ChatGPT, 573 trajectories from GPT-4 on MATH, and 6213 from ChatGPT on GSM8K. The test set sizes of MATH and GSM8K are 5000 and 1319, respectively. During testing, we feed our agent with each of the test instructions and execute the agent with a Python code interpreter. We follow the original evaluation methods of MATH and GSM8K to evaluate the performance of the agent with the accuracy of the final answer. As the evaluation results shown in Table 4, our α -UMi can still outperform the baselines on MATH and GSM8K, verifying its effectiveness. Figure 6: A case study of α -UMi. In this case, the user specifies the available tools in the instructions, making the tool invocation process simpler. The α -UMi framework completes the task within two steps through the collaboration of the planner, caller, and summarizer. <details> <summary>Image 6 Details</summary>  ### Visual Description ## Diagram: Music Search Workflow ### Overview The image depicts a step-by-step workflow of a music search process using Deezer and Shazam APIs. It starts with a user request, proceeds with planning and API calls, and concludes with a summarization of the findings. The workflow is divided into three steps, each involving different components like User, Planner, Caller, and Summarizer. ### Components/Axes * **Step 0:** * **User:** The initial request from the user. * **Planner:** The system's response and planning stage. * **Caller:** The component making the API call to Deezer. * **Observation:** The data received from the Deezer API. * **Step 1:** * **Caller:** The component making the API call to Shazam. * **Observation:** The data received from the Shazam API. * **Planner:** The system's response and planning stage. * **Step 2:** * **Planner:** The system's planning stage. * **Summarizer:** The component summarizing the findings. ### Detailed Analysis **Step 0:** * **User:** "Im a music enthusiast and I want to explore different genres and artists. Can you help me search for tracks and artists using the Deezer API? Additionally, I would like to search for songs and artists using the Shazam API." * **Planner:** "Sure! I can help you with that. We can start by using the Deezer API to search for tracks and artists. First, well make a call to the "search\_for\_deezer" function and provide the query term as "genre" or "artist" to find relevant tracks and artists. Next: caller." * **Caller:** * Action: `search_for_deezer` * Action Input: `{ "q": "genre"}` * **Observation:** * Data: `[{id: 579916362, readable: True, title: Genre, title_short: Genre, link:https://www.deezer.com/track/579916362, ...` **Step 1:** * **Caller:** * Action: `search_for_shazam` * Action Input: `{ "term": "Eddy de Pretto"}` * **Observation:** * Tracks: `{hits: [{track: {layout: 5, type: MUSIC, key: 372962066, title: Kid, subtitle: Eddy de Pretto, share: {subject: Kid - Eddy de Pretto, text: Kid by Eddy de Pretto, ...` * **Planner:** "found a track titled "Genre" by artist Eddy de Pretto using the Deezer API. Here are the details: - Title: Genre ... Now, lets move on to searching for songs and artists using the Shazam API. Next: caller." **Step 2:** * **Planner:** "Next: Conclusion." * **Summarizer:** "I found a track titled 'Genre' by artist Eddy de Prettos using the Deezer API. Here are the details: - Title: Genre - Artist: Eddy de Pretto - Link: https://www.shazam.com..." ### Key Observations * The workflow starts with a user request and involves planning, API calls to Deezer and Shazam, and summarization. * The system uses the Deezer API to search for tracks based on genre and the Shazam API to search for tracks based on artist name. * The system successfully retrieves and summarizes information about tracks and artists using both APIs. ### Interpretation The diagram illustrates a multi-step process for fulfilling a user's music search request by leveraging both Deezer and Shazam APIs. The workflow demonstrates how a system can integrate different APIs to provide a comprehensive search experience. The process begins with understanding the user's intent, planning the API calls, executing the calls, and finally summarizing the results for the user. The use of both Deezer and Shazam APIs allows the system to search based on different criteria (genre and artist), increasing the likelihood of finding relevant tracks. </details> Figure 7: A case study of α -UMi, in which α -UMi initially attempts to use the 'video\_for\_simple\_youtube\_search' to obtain detailed video information at step 0. However, it realizes that this API has broken and cannot be invoked. Therefore, the planner informs the caller to try an alternative API and obtain accurate information. Ultimately, the user's task is successfully resolved. <details> <summary>Image 7 Details</summary>  ### Visual Description ## Diagram: Multi-Step API Call Flow for YouTube Video Search ### Overview The image depicts a multi-step process involving a user request, a planner, a caller, an observation, and a summarizer to find details about a YouTube tutorial video. The diagram illustrates the flow of actions and information between these components, highlighting both successful and unsuccessful API calls. ### Components/Axes * **User:** Initiates the process with a request. * **Planner:** Determines the appropriate action and function to use. * **Caller:** Executes the API call. * **Observation:** Records the result of the API call. * **Summarizer:** Provides a summary of the findings. * **Steps:** The process is divided into steps, labeled Step 0, Step 1, and Step 2. * **Action Input:** The input provided to the API call. * **Next:** Indicates the next component in the flow. ### Detailed Analysis **Step 0:** * **User:** The user's initial request is to find a tutorial on how to draw landscapes and to get details about the most viewed landscape drawing tutorial video, including the channel that uploaded it. * **Planner:** The planner decides to use the `video_for_simple_youtube_search` function to get more details about the most viewed tutorial video. The next component is the caller. * **Caller:** The caller executes the `video_for_simple_youtube_search` action with the input `{"search": "How to Draw Easy Landscape"}`. * **Observation:** The API request fails due to a provider error. The error message indicates a problem with parsing the API response body. The process is marked with a red "X" indicating failure. **Step 1:** * **Planner:** The planner encounters an error while trying to retrieve more details about the most viewed tutorial video. The `video_for_simple_youtube_search` function failed to parse the API response. The planner will attempt to use the `search_for_simple_youtube_search` function again to find the details of the most viewed landscape drawing tutorial video. The next component is the caller. * **Caller:** The caller executes the `search_for_simple_youtube_search` action with the input `{"query": "most viewed landscape drawing tutorial"}`. * **Observation:** The API call is successful. The observation returns a JSON object containing details about the video, including: * `uploadedAt`: '8 months ago' * `id`: 'ZiYUZVqSNj4' * `tags`: {} * `nsfw`: False * `private`: False * `description`: None * `url`: 'https://www.youtube.com/watch? v=ZiYUZVqSNj4' * `unlisted`: False * `channel`: {'name': 'Uzma Rahil724', 'ratings': {'likes': 0, 'dislikes': 0}} * `shorts`: False * `title`: 'How to draw a landscape #art #landscape' * `live`: False * `duration_formatted`: '1:00' * `views`: 43043745 * `type`: 'video' The process is marked with a green checkmark indicating success. **Step 2:** * **Planner:** The planner confirms that the `search_for_simple_youtube_search` function successfully retrieved the details of the most viewed landscape drawing tutorial video. The video is titled "How to draw a landscape #art #landscape". The planner indicates that it now has the necessary information and will provide the final answer to the user. The next component is the summarizer. * **Summarizer:** The summarizer provides a summary of the findings: * The most viewed landscape drawing tutorial video is titled 'How to draw a landscape #art #landscape'. * It was uploaded 1 month ago by the channel 'Uzma Rahil724'. * The video has a duration of 1 minute and 00 seconds. * It has received 4,304,374,5 views. * You can view the video at this URL: ... The process is marked with a green checkmark indicating success. ### Key Observations * The process involves multiple steps and API calls to retrieve the desired information. * The initial API call in Step 0 fails due to a provider error. * The planner attempts a different API call in Step 1, which is successful. * The summarizer provides a concise summary of the video details. ### Interpretation The diagram illustrates a system designed to retrieve information about YouTube videos based on user queries. The system handles API errors by attempting alternative search functions. The process demonstrates a structured approach to information retrieval, involving planning, execution, observation, and summarization. The successful retrieval of video details in Step 1 and the subsequent summarization in Step 2 indicate the system's ability to fulfill the user's request, even after encountering initial errors. The use of specific functions like `video_for_simple_youtube_search` and `search_for_simple_youtube_search` suggests a modular design, where different functions are used for different types of searches or when errors occur. </details> ## Case #1 of α -UMi In ToolBench Table 5: A case of α -UMi in ToolBench. | Instruction: I want to explore the catalog of products available in the 'Electronics' category. Please provide me with the list of products along with their details. | Instruction: I want to explore the catalog of products available in the 'Electronics' category. Please provide me with the list of products along with their details. | |-------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Planner | Based on the user's request to explore the catalog of products in the 'Electronics' cate- gory, I need to call the 'get_products_in_category_for_demo_project_v13' function with the arguments 'limit': 10, 'skip': 0, and 'category': 'Electronics'. This will allow me to retrieve the list of products along with their details specifically from the 'Electronics' category. By calling this function, I can fulfill the user's request and provide them with the desired information. Next: caller. | | Caller | Action: get_products_in_category_for_demo_project_v13 Action Input: "{ "limit": 10, "skip": 0, "category": "Electronics"}" | | Observation | {'products': [{'id': 893292, 'name': 'TV', 'category': 'electronics'}, {'id': 893293, 'name': 'TV', 'category': 'electronics'}, {'id': 894481, 'name': 'Gorgeous Plastic Bench', 'category': 'Electronics'}, {'id': 896484, 'name': 'Fantastic Rubber Gloves', 'category': 'Electronics'}, {'id': 912179, 'name': 'daniel', 'category': 'electron- ics'}, {'id': 916400, 'name': 'Tasty Frozen Pants', 'category': 'Electronics'}, {'id': 917106, 'name': 'Tasty Wooden Chips', 'category': 'Electronics'}, {'id': 917821, 'name': 'Pizza', 'category': 'Electronics'}, {'id': 917875, 'name': 'Tuna', 'cate- gory': 'Electronics'}, {'id': 917998, 'name': 'Intelligent Frozen Pizza', 'category': 'Electronics'}], 'summary': {'count': 105}} | | Planner | I have retrieved the list of products available in the 'Electronics' category. Here are some of the products along with their details: 1. Product ID: 893292, Name: TV, Category: Electronics 2. Product ID: 893293, Name: TV, Category: Electronics 3. Product ID: 894481, Name: Gorgeous Plastic Bench, Category: Electronics 4. Product ID: 896484, Name: Fantastic Rubber Gloves, Category: Electronics 5. Product ID: 912179, Name: daniel, Category: Electronics 6. Product ID: 916400, Name: Tasty Frozen Pants, Category: Electronics 7. Product ID: 917106, Name: Tasty Wooden Chips, Category: Electronics 8. Product ID: 917821, Name: Pizza, Category: Electronics 9. Product ID: 917875, Name: Tuna, Category: Electronics 10. Product ID: 917998, Name: Intelligent Frozen Pizza, Category: Electronics Next: conclusion. | | Summarizer | Here are some of the products available in the 'Electronics' category: 1. Product ID: 893292, Name: TV, Category: Electronics 2. Product ID: 893293, Name: TV, Category: Electronics 3. Product ID: 894481, Name: Gorgeous Plastic Bench, Category: Electronics 4. Product ID: 896484, Name: Fantastic Rubber Gloves, Category: Electronics 5. Product ID: 912179, Name: daniel, Category: Electronics 6. Product ID: 916400, Name: Tasty Frozen Pants, Category: Electronics 7. Product ID: 917106, Name: Tasty Wooden Chips, Category: Electronics 8. Product ID: 917821, Name: Pizza, Category: Electronics 9. Product ID: 917875, Name: Tuna, Category: Electronics 10. Product ID: 917998, Name: Intelligent Frozen Pizza, Category: Electronics | ## Case #1 of Single-LLM In ToolBench Table 6: A case of Single-LLM in ToolBench. | Instruction: I want to explore the catalog of products available in the 'Electronics' category. Please provide me with the list of products along with their details. | Instruction: I want to explore the catalog of products available in the 'Electronics' category. Please provide me with the list of products along with their details. | |-------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Assistant | Based on the previous actions, it seems that the function "get_categories_for_demo_project_v13" was called to retrieve the categories, but it returned an error. To proceed differently, I will call the function "get_products_in_category_for_demo_project_v13" with the category set to "Electronics" to fetch the products in the Electronics category. Action: get_products_in_category_for_demo_project_v13 Action Input: { "limit": 10, "skip": 0, "category": "Electronics" } | | Observation | {'products': [{'id': 893292, 'name': 'TV', 'category': 'electronics'}, {'id': 893293, 'name': 'TV', 'category': 'electronics'}, {'id': 894481, 'name': 'Gorgeous Plastic Bench', 'category': 'Electronics'}, {'id': 896484, 'name': 'Fantastic Rubber Gloves', 'category': 'Electronics'}, {'id': 912179, 'name': 'daniel', 'category': 'electron- ics'}, {'id': 916400, 'name': 'Tasty Frozen Pants', 'category': 'Electronics'}, {'id': 917106, 'name': 'Tasty Wooden Chips', 'category': 'Electronics'}, {'id': 917821, 'name': 'Pizza', 'category': 'Electronics'}, {'id': 917875, 'name': 'Tuna', 'cate- gory': 'Electronics'}, {'id': 917998, 'name': 'Intelligent Frozen Pizza', 'category': 'Electronics'}], 'summary': {'count': 105}} | | Assistant | I have retrieved the list of products available in the 'Electronics' category. Here are some of the products along with their details: 1. Product ID: 893292, Name: TV, Category: Electronics 2. Product ID: 893293, Name: TV, Category: Electronics 3. Product ID: 894481, Name: Gorgeous Plastic Bench, Category: Electronics 4. Product ID: 896484, Name: Fantastic Rubber Gloves, Category: Electronics 5. Product ID: 912179, Name: daniel, Category: Electronics 6. Product ID: 916400, Name: Tasty Frozen Pants, Category: Electronics 7. Product ID: 917106, Name: Tasty Wooden Chips, Category: Electronics 8. Product ID: 917821, Name: Pizza, Category: Electronics 9. Product ID: 917875, Name: Tuna, Category: Electronics 10. Product ID: 917998, Name: Intelligent Frozen Pizza, Category: Electronics | ## Case #2 of α -UMi In ToolBench | Instruction: I am planning a vacation with my family and we are interested in scuba diving. Can you recommend some dive sites in Thailand? Additionally, we would like to know the top-tier stays available in that area for accommodation options. | Instruction: I am planning a vacation with my family and we are interested in scuba diving. Can you recommend some dive sites in Thailand? Additionally, we would like to know the top-tier stays available in that area for accommodation options. | |-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Planner | Based on the user's request, I need to recommend dive sites in Thailand and provide information about the top-tier stays in the area. To accomplish this, I will call the "esites_by_a_country_or_a_region_for_world_scuba_diving_sites_api" function with the argument "country" set to "Thailand". This will allow me to retrieve the relevant dive sites and accommodation options specifically for Thailand. By using this API, I can efficiently gather the necessary information for the user's vacation planning. Next: caller. | | Caller | Action: esites_by_a_country_or_a_region_for_world_scuba_diving_sites_api Action Input: "{ "country": "Thailand" }" | Table 7: A case of α -UMi in ToolBench. | Observation | {'data': [{'id': '0126ccc3-6d33-4cb9-a6dd-1519273acf43', 'name': 'The Rock', 'region': 'Racha Noi Island, Phuket', 'lat': '7.552420449639636', 'lng': '98.33433151245119', 'ocean': 'Indian Ocean, Andaman Sea, Strait Of Malacca', 'Location': 'Asia, Thailand, Southern Thailand, Phuket, Racha Noi Island'}, {'id': '01c6e49f-45ab-4bb4-bfee-eff9c67f9d22', 'name': 'Paradise Reef', 'region': 'Phuket, Southern Thailand', 'lat': '7.893819147272116', 'lng': '98.2584571838379', 'ocean': 'Indian Ocean, Andaman Sea, Strait Of Malacca', 'Location': 'Asia, Thailand, Southern Thailand, Phuket'}, {'id': '02446f8e-9466-4cec-8a7e-940fa2946c44', 'name': 'Shark Fin Reef', 'region': 'Payang Island, Similan Islands', 'lat': '8.50966017107524', 'lng': '97.66388654708864', 'ocean': 'Indian Ocean, Andaman Sea', 'Location': 'Asia, Thailand, Southern Thailand, Phang Nga, Similan Islands, Payang Island'}, {'id': '02dea021-ef79-4ee4-929a-46dfb03e5f6e', 'name': 'Bang Bao Reef', 'region': 'Kut Island, Trat', 'lat': '11.612304159956192', 'lng... | |---------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Planner | Based on the user's request, I need to recommend dive sites in Thailand and provide information about the top-tier stays in the area. To accomplish this, I will call the "get_top_tier_stays_filter_for_airbnb_v2" function. Since the user specifically mentioned the need for top-tier accommodations, I will call this function without any arguments to retrieve the relevant options. By using this API, I can efficiently gather the necessary information for the user's vacation planning and ensure they have a great experience during their trip. Next: caller. | | Caller | Action: get_top_tier_stays_filter_for_airbnb_v2 Action Input: "{}" | | Observation | {'status': True, 'message': 'Success', 'timestamp': 1688973421406, 'data': [{'id': '1', 'title': 'Airbnb Plus', 'subtitle': 'Every Plus home is reviewed for quality'}, {'id': '2', 'title': 'Airbnb Luxe', 'subtitle': 'Handpicked luxury homes with personally arranged services'}]} | | Planner | The user has requested recommendations for dive sites in Thailand and informa- tion about top-tier stays in the area. To fulfill their request, I called the "es- ites_by_a_country_or_a_region_for_world_scuba_diving_sites_api" function with the argument "country" set to "Thailand". This API allows me to efficiently retrieve relevant dive sites and accommodation options specifically for Thailand. By using this function, I can provide the user with accurate and up-to-date information for their vacation planning. Additionally, I called the "get_top_tier_stays_filter_for_airbnb_v2" function to retrieve the available options for top-tier accommodations. By using these APIs, I can ensure that the user has a great experience during their trip. Next: conclusion. | | Summarizer | Here are some dive sites in Thailand: The Rock in Racha Noi Island, Phuket; Paradise Reef in Phuket, Southern Thailand; Shark Fin Reef in Payang Island, Similan Islands; Bang Bao Reef in Kut Island, Trat. For accommodation options, you can consider Airbnb Plus and Airbnb Luxe, which offer high-quality and luxury homes respectively. Enjoy your scuba diving adventures in Thailand! | ## Case #2 of Single-LLM In ToolBench Instruction: I am planning a vacation with my family and we are interested in scuba diving. Can you recommend some dive sites in Thailand? Additionally, we would like to know the top-tier stays available in that area for accommodation options. | Assistant | Based on the previous action, it seems that the API call to get dive sites in Thai- land was not successful. To generate a different action, I will try using the "ranked_world_crime_cities_for_ranked_crime_cities" function to get a list of ranked crime cities. This will help me identify if Thailand is listed as a crime-free city. Action:ranked_world_crime_cities_for_ranked_crime_cities Action Input: {} | |-------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Observation | {'products': [{'id': 893292, 'name': 'TV', 'category': 'electronics'}, {'id': 893293, 'name': 'TV', 'category': 'electronics'}, {'id': 894481, 'name': 'Gorgeous Plastic Bench', 'category': 'Electronics'}, {'id': 896484, 'name': 'Fantastic Rubber Gloves', 'category': 'Electronics'}, {'id': 912179, 'name': 'daniel', 'category': 'electron- ics'}, {'id': 916400, 'name': 'Tasty Frozen Pants', 'category': 'Electronics'}, {'id': 917106, 'name': 'Tasty Wooden Chips', 'category': 'Electronics'}, {'id': 917821, 'name': 'Pizza', 'category': 'Electronics'}, {'id': 917875, 'name': 'Tuna', 'cate- gory': 'Electronics'}, {'id': 917998, 'name': 'Intelligent Frozen Pizza', 'category': 'Electronics'}], 'summary': {'count': 105}} | | Assistant | Based on the previous action, it seems that the API call to get dive sites in Thai- land was not successful. To generate a different action, I will try using the "get_city_by_filter_for_ranked_crime_cities" function to get the details of a spe- cific city in Thailand. This will help me determine if Thailand is listed as a crime-free city. Action: get_city_by_filter_for_ranked_crime_cities Action Input: {"id":1} | | Observation | {"message": "Function executing from my_tools.Travel.ranked_crime_cities.api im- port get_city_by_filter error... get_city_by_filter() got an unexpected keyword argu- ment 'id'"} | | Assistant | Based on the current state and the previous actions, it seems that the API calls to get dive sites in Thailand have been unsuccessful. To generate a different action, I will try using the "esites_by_a_country_or_a_region_for_world_scuba_diving_sites_api" function to get a list of dive sites in Thailand. This might provide the desired informa- tion about dive sites in the country. Action: esites_by_a_country_or_a_region_for_world_scuba_diving_sites_api Action Input: {"country": "Thailand"} | | Observation | {'data': [{'id': '0126ccc3-6d33-4cb9-a6dd-1519273acf43', 'name': 'The Rock', 'region': 'Racha Noi Island, Phuket', 'lat': '7.552420449639636', 'lng': '98.33433151245119', 'ocean': 'Indian Ocean, Andaman Sea, Strait Of Malacca', 'Location': 'Asia, Thailand, Southern Thailand, Phuket, Racha Noi Island'}, {'id': '01c6e49f-45ab-4bb4-bfee-eff9c67f9d22', 'name': 'Paradise Reef', 'region': 'Phuket, Southern Thailand', 'lat': '7.893819147272116', 'lng': '98.2584571838379', 'ocean': 'Indian Ocean, Andaman Sea, Strait Of Malacca', 'Location': 'Asia, Thailand, Southern Thailand, Phuket'}, {'id': '02446f8e-9466-4cec-8a7e-940fa2946c44', 'name': 'Shark Fin Reef', 'region': 'Payang Island, Similan Islands', 'lat': '8.50966017107524', 'lng': '97.66388654708864', 'ocean': 'Indian Ocean, Andaman Sea', 'Location': 'Asia, Thailand, Southern Thailand, Phang Nga, Similan Islands, Payang Island'}, {'id': '02dea021-ef79-4ee4-929a-46dfb03e5f6e', 'name': 'Bang Bao Reef', 'region': 'Kut Island, Trat', 'lat': '11.612304159956192', 'lng... | Table 8: A case of Single-LLM in ToolBench. | Assistant | Based on the current state and the previous actions, it seems that the API calls to get dive sites in Thailand have been successful. I have retrieved a list of dive sites in Thailand, including The Rock in Racha Noi Island, Phuket, Paradise Reef in Phuket, Southern Thailand, and Shark Fin Reef in Payang Island, Similan Islands. These dive sites offer potential accommodation options for your family vacation. Now, I will proceed to provide you with the final answer. conclusion:Here are some dive sites in Thailand that you might find interesting: 1. The Rock - Racha Noi Island, Phuket 2. Paradise Reef - Phuket, Southern Thailand 3. Shark Fin Reef - Payang Island, Similan Islands 4. Bang Bao Reef - Kut Island, Trat These dive sites offer potential accommodation options for your family vacation. Enjoy your scuba diving adventure in Thailand! | |-------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| ## Case In ToolAlpaca | Instruction: I'm moving to a new apartment and need to update my address with various companies. Can you first verify if my new address is valid and then standardize it? Here's the address: 321 Maple Dr, Apt 12C, Houston, TX, 77002. | Instruction: I'm moving to a new apartment and need to update my address with various companies. Can you first verify if my new address is valid and then standardize it? Here's the address: 321 Maple Dr, Apt 12C, Houston, TX, 77002. | |--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Planner | I need to verify the address and then standardize it. Next: caller. | | Caller | Action: verifyUSAddress Action Input: {"addressLine1": "321 Maple Dr", "addressLine2":"Apt 12C", "city": "Houston", "state": "TX", "zipCode": "77002"} | | Observation | {"status":"valid","standardizedAddress":{"addressLine1":"123 Main St","addressLine2":"Apt 4B","city":"New York","state":"NY","zipCode":"10001"}} | | Planner | The address is valid and has been successfully standardized. Next: conclusion. | | Summarizer | The address you provided is valid and has been successfully standardized. The standardized address is 123 Main St, Apt 4B, New York, NY, 10001 | Table 9: A case study of α -UMi in ToolAlpaca. | Case In MATH | Case In MATH | |----------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Instruction: Compute the range of the function $f(x) = sqrt{x**2}$ . | Instruction: Compute the range of the function $f(x) = sqrt{x**2}$ . | | Planner | Next: caller. | | Caller | from sympy import symbols, Interval, oo def function_range(): x = symbols('x') f = sqrt(x**2) range_f = Interval(0, oo) return range_f result = function_range() print(result) | | Observation | NameError: name 'sqrt' is not defined | | Planner | The code provided for the solution to the given problem will result in an error. To fix this error, the 'sqrt' function needs to be imported from the 'sympy' library. Next: caller. | Table 10: A case study of α -UMi in MATH. | Caller | from sympy import symbols, Interval, oo, sqrt def function_range(): x = symbols('x') f = sqrt(x**2) range_f = Interval(0, oo) return range_f result = function_range() print(result) | |-------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Observation | Interval(0, oo) | | Planner | Next: conclusion. | | Summarizer | The range of the function $f(x) = sqrt{x*2}$ is $boxed{[0, infty)}$. |