2404.14812

# Enhancing Chain of Thought Prompting in Large Language Models via Reasoning Patterns **Authors**: Yufeng Zhang, Xuepeng Wang, Lingxiang Wu1, Jinqiao Wang > Corresponding authors. Abstract Chain of Thought (CoT) prompting can encourage language models to engage in multi-step logical reasoning. The quality of the provided demonstrations significantly influences the success of downstream inference tasks. Current unsupervised CoT methods primarily select examples based on the semantics of the questions, which can introduce noise and lack interpretability. In this paper, we propose leveraging reasoning patterns to enhance CoT prompting effectiveness. Reasoning patterns represent the process by which language models arrive at their final results. By utilizing prior knowledge and prompt-based methods from large models, we first construct task-specific pattern sets. We then select diverse demonstrations based on different reasoning patterns. This approach not only mitigates the impact of noise but also provides explicit interpretability to help us understand the mechanisms of CoT. Extensive experiments demonstrate that our method is more robust and consistently leads to improvements across various reasoning tasks. Introduction Large Language Models (LLMs) have demonstrated exceptional performance across a wide range of language tasks. In general question-answering tasks (Kwiatkowski et al. 2019), LLMs hold a distinct advantage over other language models due to their robust writing capabilities. However, when it comes to more advanced tasks such as logical reasoning, mathematical computation, and symbolic reasoning, LLMs often fall short (Qiao et al. 2023; Huang and Chang 2023). <details> <summary>extracted/6276112/figures/pcot_ver1.png Details</summary>  ### Visual Description \n ## Word Problem Solutions: Step-by-Step Explanations ### Overview The image presents four separate word problems, each with a step-by-step solution. Each problem is contained within a light blue bordered box. The solutions are presented in a conversational tone, explaining the reasoning behind each step. There are visual cues (robot icons with checkmarks or crosses) indicating whether the solution is correct or incorrect. Arrows connect the problems, labeled "prompt 1", "prompt 2", and "question". ### Components/Axes The image does not contain axes or charts. It consists of text blocks and simple graphical elements. The key components are: * **Problem Statements:** Each problem begins with a "Q:" indicating the question. * **Step-by-Step Solutions:** Each problem includes a section labeled "A: Let's think step by step." which details the solution process. * **Answers:** Each problem concludes with "The answer is [value]." * **Visual Cues:** Robot icons with a checkmark (correct) or an "X" (incorrect) are used to indicate the validity of the solution. * **Arrows:** Arrows labeled "prompt 1", "prompt 2", and "question" connect the problems, suggesting a sequence. ### Detailed Analysis or Content Details **Problem 1:** * **Question:** Mark's father gave him $85. Mark bought 10 books, each of which cost $5. How much money does Mark have left? * **Solution:** * Mark has $85 and buys 10 books at $5 each. * Total spent: 10 x $5 = $50 * Money left: $85 - $50 = $35 * **Answer:** The answer is 35. * **Visual Cue:** Checkmark **Problem 2:** * **Question:** Nancy has saved 4900 cents from selling lemonade. How many dollars does Nancy have? * **Solution:** * Nancy saved 4900 cents. * Conversion to dollars: 4900 / 100 = 49 dollars. * **Answer:** The answer is 49. * **Visual Cue:** Checkmark **Problem 3:** * **Question:** The value of a sport utility vehicle this year is 16,000 dollars, which is 0.8 of what its value was last year. How much was the value of the vehicle last year? * **Solution:** * The value this year is $16,000, which is 0.8 times the value last year. * Value last year: $16,000 / 0.8 = $20,000 * **Answer:** The answer is 20,000. * **Visual Cue:** Checkmark **Problem 4:** * **Question:** The value of a sport utility vehicle this year is 16,000 dollars, which is 0.8 of what its value was last year. How much was the value of the vehicle last year? * **Solution:** * The problem tells us that the value of the sport utility vehicle this year is 16,000 dollars, which is 0.8 of what it was last year. So, if we multiply the value of the vehicle this year by 0.8, we get the value of the vehicle last year = 16,000 x 0.8 = 12,800 dollars. * **Answer:** The answer is 12,800. * **Visual Cue:** "X" ### Key Observations * The problems are presented in a pedagogical style, emphasizing step-by-step reasoning. * The visual cues (checkmarks and "X") provide immediate feedback on the correctness of the solutions. * Problem 4 contains an error in the solution. It incorrectly multiplies by 0.8 instead of dividing. * The arrows suggest a progression or sequence of problems, though the exact relationship is not explicitly stated. ### Interpretation The image is designed as an educational tool to teach problem-solving skills. The step-by-step solutions and visual cues are intended to guide learners through the process of understanding and solving word problems. The inclusion of an incorrect solution (Problem 4) serves as a valuable learning opportunity, prompting learners to identify and correct errors in reasoning. The problems cover basic arithmetic operations (addition, subtraction, multiplication, division) and unit conversions (cents to dollars). The sequence suggested by the arrows could represent increasing difficulty or a logical progression of concepts. The repetition of the same question with different solutions highlights the importance of careful reading and accurate application of mathematical principles. The use of robots adds a playful element to the learning experience. </details> Figure 1: Example of the chain-of-thought prompting. The prompt influences how LLMs arrive at the final answer. One effective approach to addressing these challenges is Chain of Thought (CoT) prompting (Wei et al. 2022b). By providing several demonstration examples that include a problem, intermediate reasoning steps, and an answer, CoT prompting serves as a contextual guide for downstream tasks. This approach encourages LLMs to generate multi-step logical reasoning, thereby maximizing the likelihood of producing more plausible answers. The advantage of this method lies in its simplicity and efficiency; unlike fine-tuning, it does not require extensive gradient updates or alter the model’s inherent capabilities. Instead, it acts as an external augmentation of knowledge. For different reasoning tasks, we can route the model to the appropriate context, and then easily switch the demonstration sets to activate the relevant knowledge and abilities in the corresponding domain. However, we argue that existing unsupervised CoT prompting methods have two major shortcomings. First, there remains a significant gap between the selected demonstration sets and the reasoning targets. Although extensive research (Zhang et al. 2023; Levy, Bogin, and Berant 2023; Yang et al. 2023; Shum, Diao, and Zhang 2023a) has explored ways to provide CoT demonstrations to enhance LLMs’ reasoning capabilities, these methods largely rely on the semantic features of the problem or the answer. Such features introduce irrelevant noise on a global scale, which can obscure the logical information needed for reasoning. Consequently, the constructed demonstration sets do not effectively represent the domain-specific logical knowledge, and struggle to adequately trigger correct reasoning in LLMs. Second, some demonstration selection methods lack interpretability and scalability. These methods are primarily based on heuristic design (Wang et al. 2022; Zheng et al. 2023) or leverage the model itself to generate additional demonstrations (Zhong et al. 2024; Yasunaga et al. 2024). The demonstration sets chosen through these means inherently lack clear explanations, making it challenging to assess their effectiveness or determine the direction for further optimization. This limitation can be particularly problematic in scenarios where interpretability is crucial. To better select a demonstration subset for a reasoning task, we believe that considering the logical patterns of reasoning is essential. Inspired by the work of (Min et al. 2022) and (Madaan, Hermann, and Yazdanbakhsh 2023), we observe that LLMs are more influenced by the templates and patterns in the context than by the correctness of the demonstrations themselves. Building on this insight, we investigate the selection of demonstrations based on Reasoning Patterns. This approach offers a dual benefit. First, it helps to eliminate bias introduced by irrelevant information, thereby reducing the gap between the demonstration set and the reasoning task. Second, it provides explicit interpretability, allowing us to gain a deeper understanding of how CoT prompting functions. This interpretability can also serve as a clue for attribution analysis and visualization. In this work, we propose Pattern-CoT https://github.com/Magicat128/Pattern-CoT., a CoT demonstration selection method based on reasoning patterns. Unlike previous approaches that focus on overall semantics, our method targets finer-grained logical reasoning operations. For instance, in mathematical reasoning, addition and multiplication represent distinct operations, while multiple sequential operators may indicate more complex operational patterns, as shown in Figure 1. Inspired by recent studies (Yang et al. 2023), a diverse range of these patterns should be incorporated into CoT. Specifically, for a given reasoning task, we first obtain a set of seed demonstrations with rationale (intermediate reasoning steps). These examples can be sourced from the training set or generated using a zero-shot approach. We then obtain specific operation tokens tailored to different task types, which help us extract reasoning patterns from the rationales. Here, we incorporate prior knowledge and guide the LLMs in generating these operation tokens. Based on the extracted reasoning patterns, we apply clustering techniques to merge similar patterns and design metrics to automatically assess the number of demonstration categories. Finally, we select representative demonstrations from each category to enrich the diversity and construct context prompts for LLMs. Notably, by incorporating task-specific knowledge, our method improves interpretability and facilitates further scalability. Our contributions can be summarized as follows: - We introduce the use of diverse reasoning patterns to enhance CoT prompting and design a demonstration selection method to reduce the gap between the demonstration set and the task. - Our method strengthens the interpretability of CoT in unsupervised scenarios, and can be utilized for further attribution analysis. - Extensive experiments demonstrate that our method consistently enhances performance across multiple reasoning tasks and various models. Related Work Chain-of-Thought Prompting Large language models have demonstrated significant ability in comprehending context and responding to prompts (Brown et al. 2020; Ouyang et al. 2022). Recent studies highlight that LLMs can achieve improved task completion without fine-tuning, particularly on reasoning tasks, when provided with few-shot demonstrations (Wei et al. 2022b). For instance, when presented with an example like Q: Mary has 9 yellow marbles. John has 3 yellow marbles. How many yellow marbles do they have in all? A: They have 9 + 3 = 12 yellow marbles. The answer is 12, LLMs are expected to emulate such a format, deconstruct the question, engage in multi-step reasoning, and refrain from generating random answers in subsequent tasks. This process is commonly referred to as chain-of-thought prompting or in-context learning (Wei et al. 2022a; Xie et al. 2022). However, implementing this practice often involves the manual design of prompts at a labour cost. Consequently, researchers are exploring more efficient example selection strategies to streamline this process. Demonstration Selection and Refinement Several CoT studies are directed towards automating the generation of demonstrations, such as retrieval-based (Rubin, Herzig, and Berant 2022), zero-shot (Kojima et al. 2022), clustering-based (Zhang et al. 2023), and self-prompt (Shao et al. 2023; Yasunaga et al. 2024). However, many of these approaches encounter challenges in achieving performance comparable to Manual-CoT, primarily due to the absence of supervision in example selection. In another branch of research, efforts are focused on enhancing the quality of CoT demonstrations. They incorporate elements such as knowledge-infusion (Zhao et al. 2023; Weng et al. 2023; Li et al. 2024), self-consistency (Wang et al. 2023a), complexity-based (Fu et al. 2022), contrastive-based (Chia et al. 2023), and progressive-hint (Zheng et al. 2023). The primary goal of these strategies is to ensure that LLMs adhere to the correct prompt and avoid being misled. <details> <summary>extracted/6276112/figures/main.png Details</summary>  ### Visual Description ## Diagram: LLM Prompting & Reasoning Process ### Overview This diagram illustrates a process for Large Language Model (LLM) prompting and reasoning, showcasing how a dataset is used to generate pattern-wise context, which then informs a downstream task. The diagram highlights a step-by-step thinking process facilitated by the LLM, including seed demonstrations, pattern discovery, and final answer generation. ### Components/Axes The diagram is divided into three main columns: "Dataset", "Pattern Wise Context", and "Downstream Task". Within each column, there are sections for questions (Q_i) and answers (A_i). A central section depicts the LLM processing flow, including K-Clustering, Embeddings, Pattern Discovery, and Prior Knowledge. Mathematical operations ("twice", "divide", "+", "=") are visually represented as connecting elements. ### Detailed Analysis or Content Details **Dataset (Left Column):** * Q₁: Liam is 16 years old now. Two years ago… * Q₂: Melanie, Sally, and Jessica each have… * Q₃: There were a total of 6 soccer games this… * Seed Demonstrations: * Q₁: Liam is 16 years old now. Two years ago, Liam’s age was twice the age of Vince. How old is Vince now? * A₁: Let’s think step by step. 2 years ago, Liam’s age was twice the age of Vince. So, we can write an equation based on this information: Liam’s age 2 years ago = 2 x Vince’s age 2 years ago. We know that Liam’s current age is 16, so his age 2 years ago would have been 16 - 2 = 14. Now, we can substitute this value into our equation: 14 = 2 x Vince’s age 2 years ago. To solve for Vince’s age, we can divide both sides of the equation by 2: 7 = Vince’s age 2 years ago. So, Vince is currently 7 + 2 = 9 years old. **Pattern Wise Context (Center Column):** * Q₁: Nancy has saved 4900 cents … (Reasoning Pattern 1) * A₁: … saved 4900 / 100 = 49 dollars … * Q₂: Tom was at the beach for 5 days … (Reasoning Pattern 2) * A₂: … a total of 7 x 5 = 35 seashells … * Q₃: Tim’s cat had kittens … (Reasoning Pattern 3) * A₃: … hem away is 9 - 3 = 6 … * K-Clustering: Visual representation of clustering with labeled points. * Embeddings: Visual representation of embeddings. * Pattern Discovery: Visual representation of pattern discovery. * Prior Knowledge: Visual representation of prior knowledge. * Mathematical Operations: "twice", "divide", "+", "=" are visually connected. **Downstream Task (Right Column):** * Question: The value of a sport utility vehicle this year is $16,000, which is 0.8 of what its value was last year. How much is the value of the vehicle last year? * Final Answer: The problem tells us that the value of the sport utility vehicle this year is $16,000, which is 0.8 times its value last year. This means that the value last year is $16,000 / 0.8 = $20,000. The answer is $20,000. **LLM Prompting Flow (Central Area):** * "Let's think step by step" is prominently displayed. * "LLM Prompting" is labeled above the flow. * "7+3=29 years old" is displayed at the bottom. ### Key Observations * The diagram demonstrates a multi-step reasoning process. * The "Pattern Wise Context" section shows examples of different reasoning patterns. * The downstream task involves a simple division problem. * The LLM is presented as a central component facilitating the reasoning process. * The diagram uses visual cues (arrows, connecting lines) to illustrate the flow of information. ### Interpretation The diagram illustrates a methodology for improving LLM performance by providing structured context and guiding the model through a step-by-step reasoning process. The "Dataset" provides initial examples, the "Pattern Wise Context" extracts underlying reasoning patterns, and the "Downstream Task" applies these patterns to solve a new problem. The LLM acts as the engine for this process, leveraging techniques like K-Clustering and embeddings to identify and apply relevant knowledge. The inclusion of mathematical operations as visual elements suggests that the LLM is capable of performing quantitative reasoning. The "Let's think step by step" prompt is a key element, encouraging the model to articulate its reasoning process, which can improve accuracy and transparency. The final answer demonstrates the successful application of this methodology to solve a practical problem. The diagram suggests a focus on teaching the LLM *how* to reason, rather than simply providing it with facts. </details> Figure 2: Illustration of our proposed framework. We first extract different patterns from the original rationales. Then clustering is used to produce a group of demonstrations. This enables LLMs to perceive diverse reasoning patterns and to select a proper solution path. It avoids LLMs being biased by monotonous reasoning mode. Role of In-Context Patterns To understand the underlying mechanism of ICL, (Min et al. 2022) and (Madaan, Hermann, and Yazdanbakhsh 2023) employ counterfactual prompting methods. These methods involve substituting question-answer mapping, token distributions, answer patterns, and many other factors. Their findings consistently show that the correctness of examples is not the most crucial factor, but rather the distribution or pattern (e.g. equations, templates, sentence structure) of the examples. In this paper, we continue to uncover the power of CoT patterns and show how they can improve the reasoning process. Methodology We now explore the impact of diverse demonstration reasoning patterns on chain-of-thought prompting. According to (Min et al. 2022), the precision of demonstrations is not crucial when LLMs engage in ICL. Even if all the demonstrations provided are incorrect, it would only marginally impede performance. This aligns with the insight derived from Auto-CoT (Zhang et al. 2023): clustering zero-shot question-answer pairs without emphasizing accuracy can still yield valuable examples. Consequently, our focus shifts to a more nuanced factor - the underlying reasoning pattern that harbours more informative content (Madaan, Hermann, and Yazdanbakhsh 2023) - to evaluate its potential benefits for the CoT process. The entire process is summarized in Figure 2 and Algorithm 1. Seed Demonstration Collection For a given task $Q=\{q_{1},q_{2},...,q_{N}\}$ with $N$ questions, we first need to obtain their rationales and answers $\{q_{i},r_{i},a_{i}\}$ that can be used as context for CoT prompting. For data from existing training sets, we can directly use the training data. However, in practical applications, complete training sets may not always be available. In such cases, we refer to methods like (Zhang et al. 2023; Shum, Diao, and Zhang 2023b) and leverage the zero-shot (Kojima et al. 2022) capabilities of LLMs to generate the corresponding rationales. It is important to note that we do not require the answers to be correct or labelled; our focus is on whether the generated rationales contain meaningful reasoning patterns. Pattern Discovery Based on the rationale set $Ra=\{r_{1},r_{2},...,r_{N}\}$ that we have obtained, we next identify the reasoning operations $T$ associated with the task. For tasks with a relatively limited action space, we can define reasoning operations using prior knowledge, as these operations represent the fundamental units of reasoning tasks. For example, in arithmetic problems, we refer to a glossary of possible operators from sources like Wikipedia The glossary of arithmetic operators refers to the Wikipedia: https://en.wikipedia.org/wiki/Glossary_of_mathematical_symbols, including basic arithmetic operations, square roots, comparison symbols, etc. For tasks with less clearly defined operations, we adapt definitions from arithmetic problems to guide LLMs in generating the corresponding reasoning operations. We design the prompt as: ‘Similar to operators used in arithmetic such as (+, -, *, /), which operators do you think best represent the [TASK]? Example of [TASK]: …’ For each rationale $r_{i}∈ Ra$ , we extract the reasoning operation tokens or phrases $t_{j}∈ T$ to form its reasoning pattern: $$ p_{i}=f(r_{i},T)=\{t_{i1},t_{i2},...,t_{ij}\} \tag{1} $$ where $f$ denotes the function used to extract the reasoning path. In this context, $p_{i}$ represents how LLMs apply these operations step-by-step to reach the final result, and $t_{ij}$ can repeated. Algorithm 1 Pattern-CoT Demonstration Selection 0: A set of task questions $Q$ 0: Demonstration list $d=[d_{1},d_{2},...,d_{k}]$ 1: Acquire operation token set $T$ with LLMs prompting or domain knowledge based on $Q$ 2: for $q_{i}∈ Q$ do 3: Generate rationale $r_{i}$ with Zero-Shot-CoT 4: $p_{i}=[]$ 5: for each token $t_{ij}∈ r_{i}$ do 6: if $t_{ij}∈ T$ then 7: Update $p_{i}$ with $t_{ij}$ 8: end if 9: end for 10: $\widetilde{p}_{i}=\mathrm{encode}(p_{i})$ 11: end for 12: Select proper $k$ 13: Cluster all $[\widetilde{p}_{1},\widetilde{p}_{2},...,\widetilde{p}_{i}]$ into $k$ clusters 14: Sample $d=[d_{1},d_{2},...,d_{k}]$ from each cluster 15: return $d$ Pattern Wise Demonstration Selection Once we have identified the task-relevant patterns, we use them to select better demonstration sets. Following (Zhang et al. 2023), we cluster all the $p_{i}$ patterns while preserving diversity. Although $p_{i}$ is a simplified sequence of tokens, it still contains substantial semantic information that can be used to uncover underlying similarities. For instance, a sequence of addition operations is likely to be closer to a single addition operation than to a single multiplication operation. To leverage this, we use a language model to encode these patterns. We then apply the $k$ -means clustering algorithm to generate $k$ clusters and sample from each cluster: $$ \widetilde{p}_{i}=\mathrm{encode}(p_{i}) \tag{2} $$ $$ c_{1},c_{2},...,c_{k}=\mathrm{cluster}(\widetilde{p}_{1},\widetilde{p}_{2},...% ,\widetilde{p}_{i}) \tag{3} $$ $$ d=\{{q_{m},r_{m},a_{m}}|\widetilde{p}_{m}\in c_{m},m=1,2,...,k\} \tag{4} $$ where $d$ denotes the demonstration set, $c_{k}$ denotes the $k$ -th cluster. Specifically, we use patterns primarily to select demonstrations rather than directly as context for downstream tasks. We utilize the original problem $q_{k}$ and rationale $r_{k}$ corresponding to the $p_{k}$ patterns as the CoT input. Number of Demonstrations Since previous methods lack knowledge-based guidance, the choice of $k$ is often based on heuristic values. However, having too many demonstrations does not proportionally enhance the performance (Wei et al. 2022b; Agarwal et al. 2024), while too few may fail to adequately capture the task’s characteristics. By incorporating reasoning operations, we can use the number of these operations to inform a more reasonable choice for $k$ : $$ k=\lceil\frac{1}{2}\times n\times(1+\mathrm{log}(N))\rceil \tag{5} $$ where $n$ denotes the number of identified operations, and $\lceil\rceil$ represents the ceiling function that rounds up to the nearest integer. This formula empirically takes into account the impact of the number of operation types on the number of demonstrations and further adjusts based on the sample size. Experiments In this section, our objective is to evaluate the effectiveness of our proposed method and answer the following research questions: - RQ1: Does incorporating reasoning patterns enhance the effectiveness of CoT prompting? - RQ2: How do the reasoning patterns influence the outputs of LLMs? - RQ3: Is our method robust and scalable to other models? | Dataset | Samples | Operation Tokens | | --- | --- | --- | | GSM8K | 1319 | $+,-,×,/$ | | ‘more’, ‘less’, ‘twice’, ‘half’ | | | | AQuA | 254 | $+,-,×,/,\pi,\sqrt{x},x^{n},x^{\circ},log$ | | MultiArith | 600 | $+,-,×,/$ | | AddSub | 395 | | | SingleEq | 508 | | | SVAMP | 1000 | | | Coin | 500 | ‘heads up’, ‘tails up’ | | Date | 369 | ‘day’, ‘week’, | | ‘month’, ‘year’ | | | | ‘yesterday’, ‘tomorrow’ | | | Table 1: The number of samples and operation tokens. Experimental Setup Datasets. We adopt eight representative datasets for our reasoning tasks: MultiArith (Roy and Roth 2015), GSM8K (Cobbe et al. 2021), AddSub (Hosseini et al. 2014), AQUA-RAT (Ling et al. 2017), SingleEq (Koncel-Kedziorski et al. 2015), SVAMP (Patel, Bhattamishra, and Goyal 2021), Coin-Flip (Wei et al. 2022b), and BIG-bench Date Understanding (Srivastava et al. 2023). They require certain reasoning steps and are commonly used for CoT method comparisons (Wei et al. 2022b; Kojima et al. 2022; Zhang et al. 2023; Wang et al. 2023b; Fu et al. 2022). For tasks MultiArith, AddSub, SingleEq, and SVAMP, we define the set of operation tokens based on a glossary from Wikipedia, as the operations involved are relatively straightforward. For tasks GSM8K and AQUA, we expand the operation token vocabulary manually based on data distribution. For tasks Coin-Flip and BIG-bench Date Understanding, we prompt GPT-4 to generate the corresponding operation tokens. The specific details of the datasets can be found in Table 1. | LLaMA-2 Model 7b-chat-hf (+ SC) | MultiArith Zero-Shot-CoT 79.83 | GSM8K 72.33 27.14 | AddSub 21.00 62.78 | AQuA 57.97 21.65 | SingleEq 24.01 68.11 | SVAMP 57.67 47.60 | Coin 41.90 52.80 | Date 44.60 40.37 | 39.29 | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | Random-CoT | 76.16 | 24.41 | 65.59 | 22.44 | 66.14 | 46.59 | 48.00 | 44.44 | | | Auto-CoT | 76.00 | 26.99 | 58.48 | 24.01 | 64.96 | 43.80 | 51.20 | 44.71 | | | Auto-CoT-RA | 74.83 | 26.76 | 63.29 | 23.80 | 66.92 | 45.19 | 48.00 | 43.08 | | | Ours | 79.66 | 27.45 | 65.06 | 28.34 | 71.85 | 48.50 | 59.40 | 45.79 | | | Ours (Adaptive $k$ ) | 79.66* | 28.05 | 67.08 | 29.13 | 71.85* | 48.50* | 58.40 | 46.34 | | | 13b-chat-hf | Zero-Shot-CoT | 77.50 | 34.49 | 60.75 | 15.74 | 69.29 | 49.40 | 47.40 | 46.07 | | Auto-CoT | 82.16 | 36.77 | 63.03 | 25.19 | 70.67 | 55.50 | 54.20 | 53.93 | | | Auto-CoT-RA | 82.16 | 37.04 | 62.08 | 27.74 | 66.14 | 52.10 | 62.80 | 54.47 | | | Ours | 83.16 | 37.68 | 65.82 | 26.37 | 74.80 | 56.39 | 57.40 | 56.91 | | | Ours (Adaptive $k$ ) | 83.16* | 38.44 | 64.81 | 31.49 | 74.80* | 56.39* | 67.80 | 60.97 | | Table 2: Accuracy (%) on eight reasoning datasets. We present the mean value obtained from five runs. * denotes the situation where $k$ does not change, and results are copied from above. For the Random-CoT method, we report the best result since we are concerned about the potential of CoT. For the self-consistency method, we set the number of paths as 5 (Wang et al. 2023a). Language Models. To facilitate subsequent interpretability analysis, we select open-source models as our reasoning engine. Specifically, we use models from the LLaMA-2 family due to their foundational logical reasoning capabilities and support for CoT prompting. These models are deployed on our local server, which is equipped with 8 RTX 3090 GPUs, each with 24GB of memory. Due to hardware constraints, we test only the 7B and 13B models. Experiments with larger models or those from other families are discussed in subsequent sections. We use the inference functions of these models, and the process does not involve training or fine-tuning. Additionally, we set the hyperparameters with a temperature of 0.4 and top_p of 0.9 to manage the model’s randomness (Xu et al. 2022). To maintain consistency with (Zhang et al. 2023), we use Sentence-BERT (Reimers and Gurevych 2019) as our encoder and select the ‘all-MiniLM-L6-v2’ model for semantic vector representation. This model has also been proven effective in our experiments. Baselines. We primarily compare our methods with unsupervised methods including Zero-Shot-CoT (Kojima et al. 2022), Random-CoT, Auto-CoT (Zhang et al. 2023), and Self-Consistency (Wang et al. 2023a). Building on Auto-CoT, we introduce an additional variant, Auto-CoT-RA, which replaces the original question embedding with the rationale embedding for clustering. The purpose of this modification is to investigate whether this subtle shift can implicitly uncover the underlying patterns in reasoning. Unless otherwise specified, our method uses the same $k$ value as the baseline in experiments. Additionally, we conduct experiments using our method with the adaptive $k$ value that we designed. Main Results (RQ1) Table 2 presents the overall performance of various methods on the 7B and 13B models. Since our primary goal is to evaluate whether focusing on diverse patterns provides more benefit to reasoning than semantic information, we are not concerned with identifying which model achieves state-of-the-art performance. Based on these results, we make the following observations: - Overall, our method consistently outperforms the baseline approaches. This stable improvement indicates that by introducing diverse reasoning patterns, we can identify more representative demonstration sets, where each example embodies a different reasoning strategy. Using these diverse examples as context for LLMs can further enhance their ability to solve downstream tasks. - We observe that for arithmetic problems with a limited set of operation tokens, such as MultiArith, AddSub, SingleEq, and SVAMP, our method achieves more significant improvements compared to methods based on semantic information. This suggests that the demonstration sets we construct can effectively cover the majority of reasoning paths, thereby providing comprehensive guidance for LLMs to select appropriate reasoning patterns. - For datasets with a relatively broader action space, like GSM8K and AQuA, the improvements are less significant. This implies that a limited number of examples do not fully capture the diversity of reasoning patterns. However, when we recalculate the number of clusters using adaptive $k$ and expand the demonstration set, we observe additional gains on these two datasets. - Surprisingly, we find that for datasets like Coin and Date, where the operation patterns are not explicitly defined, our method actually lead to greater improvements. We hypothesize that this is because the questions in these datasets are quite similar, making it difficult to distinguish them based on semantic features alone. In contrast, leveraging reasoning patterns allows us to identify representative examples from a different dimension. Additionally, both of these datasets show further performance improvements when using adaptive $k$ . <details> <summary>extracted/6276112/figures/subset.png Details</summary>  ### Visual Description \n ## Bar Chart: The Accuracy of Different Operation Sets ### Overview This bar chart compares the accuracy of three different operation sets – basic operation subset, supplemental subset, and full set – across two datasets: GSM8K and AQUA. The accuracy is measured on the y-axis, ranging from 23 to 30. The x-axis represents the datasets. ### Components/Axes * **Title:** "The Accuracy of Different Operation Sets" (centered at the top) * **X-axis Label:** "Dataset" (centered at the bottom) * **Y-axis Label:** "Accuracy" (left side, vertical) * **Y-axis Scale:** Ranges from 23 to 30, with tick marks at integer values. * **Legend:** Located in the top-left corner. * "basic operation subset" - represented by dark gray bars. * "supplemental subset" - represented by light blue bars. * "full set" - represented by light red bars. ### Detailed Analysis The chart consists of six bars, grouped by dataset. **GSM8K Dataset:** * **basic operation subset:** The dark gray bar has a height of approximately 25.6. * **supplemental subset:** The light blue bar has a height of approximately 25.9. * **full set:** The light red bar has a height of approximately 27.4. **AQUA Dataset:** * **basic operation subset:** The dark gray bar has a height of approximately 25.2. * **supplemental subset:** The light blue bar has a height of approximately 27.8. * **full set:** The light red bar has a height of approximately 28.6. The bars for the "full set" are consistently the highest for both datasets, indicating the highest accuracy. The "supplemental subset" consistently outperforms the "basic operation subset". ### Key Observations * The "full set" consistently achieves the highest accuracy across both datasets. * Accuracy is generally higher on the AQUA dataset compared to the GSM8K dataset for all operation sets. * The difference in accuracy between the "basic operation subset" and the "supplemental subset" is relatively small for GSM8K, but more pronounced for AQUA. ### Interpretation The data suggests that using the "full set" of operations leads to the best performance in terms of accuracy for both GSM8K and AQUA datasets. This indicates that incorporating all available operations provides a more comprehensive and effective approach. The higher accuracy observed on the AQUA dataset might be due to the inherent characteristics of the dataset itself, potentially being more amenable to the full set of operations. The improvement from the "basic operation subset" to the "supplemental subset" suggests that adding supplemental operations is beneficial, but the greatest gains are realized when utilizing the complete operation set. This could imply diminishing returns with each additional operation, or that the "full set" captures crucial interactions between operations that are missed in the subsets. The difference in performance between the datasets suggests that the optimal operation set might be dataset-dependent. </details> Figure 3: Comparison of different operation sets. | Q: Mark’s father gave him $85. Mark bought 10 books, each of which cost $5. How much money does Mark have left? A: Let’s think step by step. If Mark has $85 and he buys 10 books at $5 each, then he will spend a total of $50 (10 x $5). So, after buying the books, Mark will have $35 ($85 - $50) left. The answer is 35. | Q: Tom was at the beach for 5 days and found 7 seashells every day. How many seashells did Tom find during the beach trip? A: Let’s think step by step. If Tom found 7 seashells every day, then he found a total of 7 x 5 = 35 seashells during his entire beach trip. The answer is 35. | | --- | --- | | Q: Mike has 48 books. Alyssa has 8 times more books than Mike. How many dozen books does Alyssa have? A: Let’s think step by step. If Mike has 48 books, then Alyssa has 8 times that number = 8 x 48 = 384 books. So Alyssa has 384 books. The answer is 384. | Q: Nancy has saved 4900 cents from selling lemonade. How many dollars does Nancy have? A: Let’s think step by step. Nancy saved 4900 cents, which means she saved 4900 / 100 = 49 dollars. The answer is 49. | | Q: The value of a sport utility vehicle this year is 16,000 dollars , which is 0.8 of what its value was last year. How much is the value of the vehicle last year? | Q: The value of a sport utility vehicle this year is 16,000 dollars , which is 0.8 of what its value was last year. How much is the value of the vehicle last year? | | A: Let’s think step by step. The problem tells us that the value of the sport utility vehicle this year is 16,000 dollars, which is 0.8 of what it was last year. So, if we multiply the value of the vehicle this year by 0.8, we get the value of the vehicle last year = 16,000 x 0.8 = 12,800 dollars. The answer is 12,800. | A: Let’s think step by step. The problem tells us that the value of the sport utility vehicle this year is $16,000, which is 0.8 times its value last year. This means that the value last year is $16,000 / 0.8 = $20,000. The answer is $20,000. | Table 3: Case study of Auto-CoT and our method for demonstration selection and downstream inference. The upper questions and answers are demonstrations constructed by two methods, and the lower part contains how LLMs solve the inference task. | MultiArith | 8 | 2 | 25.0% | | --- | --- | --- | --- | | GSM8K | 8 | 5 | 62.5% | | AddSub | 8 | 3 | 37.5% | | AQuA | 4 | 4 | 100% | | SingleEq | 8 | 2 | 25.0% | | SVAMP | 8 | 6 | 75% | | Coin | 8 | 3 | 37.5% | | Date | 8 | 1 | 12.5% | Table 4: The number of demonstrations and their error rate for each dataset. There are several additional observations. For instance, in some cases, Auto-CoT-RA outperforms Auto-CoT, while in others it does not. This suggests that simply shifting from question semantics to rationale semantics does not necessarily narrow the gap between demonstrations and the reasoning task. Deeper reasoning patterns can still be obscured by irrelevant information. Moreover, in certain situations, using a random demonstration set can also surpass Auto-CoT, although this improvement is inconsistent. This indirectly highlights that other factors, such as underlying reasoning patterns, can influence the effectiveness of examples. Our method, in most cases, demonstrates a more stable ability to uncover these factors. Impact of Operation Tokens (RQ1) To further assess the impact of reasoning patterns, we conduct additional experiments. Given that GSM8K and AQuA datasets utilize additional operation tokens, we removed some of these tokens to determine their influence. Specifically, we categorize the expanded operation tokens into a basic operation subset, such as $\{+,-,×,/\}$ , similar to other arithmetic tasks, and the remaining tokens as supplementary subsets. These subsets represent only a portion of the reasoning patterns within these two datasets. Figure 3 shows the results of using different subsets on the 7B model. The experimental results demonstrate that using operation subsets as reasoning pattern tokens can degrade overall performance. The primary reason for this is that these subsets do not sufficiently cover the task’s logical scope. It leads to a lack of diversity. However, when the full set of operations is utilized, a broader range of scenarios can be activated, allowing the model to better adapt to the task. Case Study (RQ2) To gain a deeper understanding of CoT prompting, we perform a case study. Table 3 presents a typical instance analysis. We observe that Auto-CoT, due to its introduction of numerous irrelevant patterns, tends to distort the reasoning results of LLMs. In contrast, our method, which includes a diverse set of reasoning pattern templates, enables the model to generate correct responses. <details> <summary>extracted/6276112/figures/Figure_case1.png Details</summary>  ### Visual Description \n ## Heatmap: Token Attribution ### Overview The image presents a heatmap displaying "Token Attribution" values. The heatmap is a 9x5 grid, with labels indicating mathematical expressions along the top edge and symbols representing variables (x, =, -) along the left edge. The color scale ranges from -1.0 (dark red) to 1.0 (dark green), representing the strength of the attribution. ### Components/Axes * **X-axis (Top):** Labeled with mathematical expressions: "8 x 48 = 384", "3 x 4 = 12", "6 - 4 = 2", "50 (10x5)", "$35 ($85 - $50)". * **Y-axis (Left):** Labeled with symbols: "-", "1", "6", "0", "0", "0", "-x", "0", "8", "=". * **Color Scale (Right):** Ranges from -1.0 (red) to 1.0 (green), with intermediate values at -0.5 and 0.5, and 0.0. Labeled "Token Attribution". * **Grid:** 9 rows and 5 columns of colored cells, each representing a value. ### Detailed Analysis The heatmap displays numerical values within each cell, corresponding to the intersection of the X and Y axis labels. Here's a breakdown of the values, row by row: * **Row 1 (-):** 0.3452, 0.2580, 0.5823, 0.0161, -0.0472 * **Row 2 (1):** 0.0811, -0.0066, -0.0037, 0.3934, -0.0356 * **Row 3 (6):** 0.0234, 0.0145, 0.0512, 0.0243, 0.0135 * **Row 4 (0):** 0.0049, -0.0019, -0.0196, -0.0211, -0.0011 * **Row 5 (0):** 0.0000, 0.0000, -0.0000, 0.0000, -0.0001 * **Row 6 (0):** 0.0001, 0.0002, 0.0002, -0.0001, -0.0002 * **Row 7 (-x):** 1.3714, 0.6865, 0.1771, 0.6837, -0.3675 * **Row 8 (0):** 0.0089, 0.0134, 0.0232, -0.0061, -0.0099 * **Row 9 (8):** -0.0054, 0.0008, 0.0012, 0.0025, 0.0016 * **Row 10 (=):** -0.0007, 0.0015, 0.0007, -0.0036, 0.0009 * **Row 11 (=):** 0.0081, 0.0104, 0.0097, -0.0127, 0.0004 **Trends:** * The "-x" row (row 7) exhibits the highest positive attribution values, particularly for "8 x 48 = 384" (1.3714) and "3 x 4 = 12" (0.6865). * The expression "$35 ($85 - $50)" consistently shows negative attribution values across most rows. * Rows labeled "0" generally have values close to zero. * The values are generally small, with a few outliers. ### Key Observations * The highest positive value is 1.3714, associated with "8 x 48 = 384" and "-x". * The lowest negative value is -0.3675, associated with "$35 ($85 - $50)" and "-x". * The heatmap shows a clear distinction between positive and negative attribution values, with the color gradient effectively representing the strength of the attribution. * The mathematical expressions along the x-axis seem to have varying degrees of influence on the token attribution, as indicated by the different color intensities. ### Interpretation This heatmap likely represents the attribution scores of different tokens (mathematical expressions) to a specific feature or concept represented by the rows (symbols). The higher the attribution score (closer to 1.0), the more strongly that token contributes to the feature. Conversely, negative attribution scores (closer to -1.0) indicate a negative contribution. The strong positive attribution of "8 x 48 = 384" to "-x" suggests that this expression is highly relevant to the concept represented by "-x". The negative attribution of "$35 ($85 - $50)" across multiple rows indicates that this expression might be acting as a distractor or having an opposing effect. The heatmap provides insights into the relationships between mathematical expressions and underlying concepts, potentially aiding in understanding the model's reasoning or identifying areas for improvement. The fact that many values are close to zero suggests that most expressions have a relatively weak attribution to the features being analyzed. The heatmap is a visualization of feature importance, showing which mathematical expressions are most influential in determining the values of the symbols. </details> <details> <summary>extracted/6276112/figures/Figure_case2.png Details</summary>  ### Visual Description \n ## Heatmap: Token Attribution for Mathematical Expressions ### Overview The image presents a heatmap visualizing "Token Attribution" values for different parts of several mathematical expressions. The expressions are arranged in a grid-like structure, and each cell in the grid is colored based on its corresponding attribution score. The color scale ranges from -1.5 (dark red) to 1.5 (dark green), with 0 represented by a light yellow/white. ### Components/Axes * **X-axis:** Represents the different mathematical expressions: "9 + 3 = 12", "7 x 5 = 35", "4900 / 100 = 49", "9 - 3 = 6", "6 - 6 = 0". * **Y-axis:** Represents the tokens within each expression. The tokens are numerical digits, operators (+, x, -, /), and the equals sign (=). The Y-axis labels are: "9", "1", "6", "0", "0", "0", "3", "8", "=". * **Color Scale (Legend):** Located on the right side of the heatmap. It indicates the mapping between color and "Token Attribution" value. * -1.5: Dark Red * -1.0: Red * -0.5: Orange * 0.0: Yellow/White * 0.5: Light Green * 1.0: Green * 1.5: Dark Green ### Detailed Analysis The heatmap displays attribution scores for each token in each expression. Here's a breakdown of the values, organized by expression and token: **1. 9 + 3 = 12** * 9: -0.0756 * 1: 0.0198 * 6: 0.0001 * 0: 0.0017 * 0: 0.0005 * 0: 0.0000 * 3: 0.0003 * 8: 0.0005 * =: 0.0017 **2. 7 x 5 = 35** * 7: 0.0216 * 1: -0.0441 * 6: -0.0004 * 0: -0.048 * 0: -0.0001 * 0: 0.0000 * 5: 0.0006 * 8: 0.0003 * =: 0.0026 **3. 4900 / 100 = 49** * 4: 0.0065 * 1: -0.0987 * 6: -0.0004 * 0: -0.0048 * 0: -0.0001 * 0: 0.0000 * 9: 0.0005 * 8: 0.0005 * =: 0.0024 **4. 9 - 3 = 6** * 9: 0.1044 * 1: 0.0141 * 6: 0.0000 * 0: -0.0003 * 0: 0.0000 * 0: 0.0000 * 3: 0.0000 * 8: 0.0001 * =: 0.0007 **5. 6 - 6 = 0** * 6: -0.0691 * 1: -0.0096 * 6: -0.0070 * 0: -0.0002 * 0: 0.0001 * 0: 0.0000 * 0: 0.0000 * 8: 0.0002 * =: 0.0021 **Notable High Attribution Values:** * The token '9' in the expression "9 - 3 = 6" has a high positive attribution score of 0.1044. * The token '3' in the expression "9 + 3 = 12" has a positive attribution score of 0.0003. * The token '1' in the expression "7 x 5 = 35" has a negative attribution score of -0.0441. * The token '1' in the expression "4900 / 100 = 49" has a negative attribution score of -0.0987. * The token '1' in the expression "9 - 3 = 6" has a positive attribution score of 0.0141. * The token '1' in the expression "6 - 6 = 0" has a negative attribution score of -0.0096. * The token '9' in the expression "9 + 3 = 12" has a negative attribution score of -0.0756. **Highest Attribution Value:** * The highest attribution value is 1.9074, located at the intersection of the Y-axis token '1' and the X-axis expression "4900 / 100 = 49". ### Key Observations * Attribution scores are generally low in magnitude, mostly falling between -0.1 and 0.1. * There is a mix of positive and negative attribution scores, suggesting that some tokens contribute positively to the model's prediction, while others contribute negatively. * The expression "4900 / 100 = 49" has a particularly high attribution score for the token '1', indicating that this token is highly influential in the model's prediction for this expression. * The token '6' consistently shows low or negative attribution scores across multiple expressions. ### Interpretation This heatmap visualizes the importance of each token (digit, operator, equals sign) within different mathematical expressions, as determined by a model's attribution mechanism. The attribution score indicates how much each token contributes to the model's prediction or output. The high attribution score for '1' in "4900 / 100 = 49" suggests that the model heavily relies on this digit to correctly solve the division problem. The negative attribution scores for certain tokens might indicate that the model is learning to suppress or ignore those tokens in specific contexts. For example, a negative attribution for '6' could mean the model is learning that '6' is less relevant in the context of the given expressions. The overall pattern suggests that the model is sensitive to the specific digits and operators used in each expression, and it assigns different levels of importance to each token based on its contribution to the overall calculation. This type of analysis can be used to understand how the model is reasoning about mathematical expressions and to identify potential areas for improvement. The heatmap provides a visual representation of the model's internal decision-making process, offering insights into its strengths and weaknesses. </details> Figure 4: Visualization of token attribution for the case study. The left part stands for the score matrix of patterns from Auto-CoT, and the right part stands for the score matrix from our method. The upper column denotes each individual prompt, and the row denotes the generated token sequence. Higher scores (positive) indicate that the input has a greater impact on the output. Feature Attribution (RQ2) Following the previous case study, we seek to understand why different contextual reasoning patterns alter the output of LLMs. Specifically, we employ a perturbation-based feature attribution analysis method (Winter 2002) to aid in this understanding. Traditional attention-based analysis methods have been criticized for their inability to identify the most significant features (Wiegreffe and Pinter 2019; Zhao et al. 2024), which is why we turned to this perturbation-based approach. By masking portions of the input tokens, we recompute the generation probabilities for each output token to assess the input’s attribution impact on these output tokens. We use Captum (Miglani et al. 2023) to achieve this visualization. Figure 4 presents the attribution analysis matrix for the case study. According to the visualization results, we find that when a particular pattern is overly dense in the examples, the model tends to activate related knowledge, which can lead to biased reasoning processes. Conversely, when these patterns are more diverse, the model is more likely to activate the correct reasoning pathways. Our method, by enhancing the diversity of patterns in the demonstrations, effectively reduces the distance to the reasoning task objectives. | GPT-3.5 Auto-CoT Ours | Zero-Shot 81.26 83.54 | 83.29 58.66 62.38 | 59.44 91.53 93.11 | 90.55 | | --- | --- | --- | --- | --- | | Qwen | Zero-Shot | 54.93 | 35.03 | 69.07 | | Auto-CoT | 62.53 | 30.31 | 80.31 | | | Ours | 67.59 | 33.46 | 82.08 | | Table 5: Result of GPT-3.5-turbo-0125 and Qwen-7b-chat model on different datasets. Error Robustness (RQ3) It is worth mentioning that we do not impose supervision on the labels of the demonstrations. Therefore, we proceed to count the number of incorrect instances within the selected set, as shown in Table 4. It is intriguing to notice that the majority of our provided demonstrations are imperfect, with AQuA even exhibiting a 100% error rate. This phenomenon suggests that LLMs struggle to discern incorrect instances from correct ones. Instead, they learn from how the example approaches problem-solving, which we refer to as ‘pattern’. Our method encourages LLMs to follow the most probable reasoning chain towards the final answer and thus leads to a significant improvement. Results on Other Models (RQ3) To determine whether our method is applicable to different models, we test it on various LLM branches. Specifically, we select the GPT series to represent larger and more advanced models, and Qwen to represent multilingual models. For the sake of hardware resources and budget constraints, we experiment with the GPT-3.5-turbo and Qwen-7B models. Table 5 presents the performance of several methods on these models. Notably, the experiments show that Auto-CoT, in some cases, underperforms compared to direct answering on these models. We attribute this to the inherent noise in semantics-based methods. Our approach mitigates this noise, resulting in more consistent performance improvements. Conclusion This paper aims to address the noise issue inherent in unsupervised semantic-based CoT methods and proposes a reasoning pattern-based approach for CoT demonstration selection. Our method explicitly enhances the interpretability of reasoning processes and illustrates how LLMs can be guided toward generating accurate answers. Extensive experiments validate the effectiveness, robustness, and compatibility of our approach. Acknowledgements This work was supported by the National Key R&D Program of China (Grant No.2023ZD0120400), Beijing Natural Science Foundation (L247028), National Natural Science Foundation of China (No. 62276260, 62076235), Beijing Municipal Science and Technology Project (Z231100007423004). We sincerely thank all reviewers and ACs for their insightful comments, time and efforts. References - Agarwal et al. (2024) Agarwal, R.; Singh, A.; Zhang, L. M.; Bohnet, B.; Rosias, L.; Chan, S.; Zhang, B.; Anand, A.; Abbas, Z.; Nova, A.; Co-Reyes, J. 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