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# Self-Alignment for Factuality: Mitigating Hallucinations in LLMs via Self-Evaluation **Authors**: Tencent AI Lab, Bellevue, {zhangxy, jyzhou, hmmeng}@se.cuhk.edu.hk Abstract Despite showing impressive abilities, large language models (LLMs) often struggle with factual inaccuracies, i.e., “hallucinations”, even when they hold relevant knowledge. To mitigate these hallucinations, current approaches typically necessitate high-quality human factuality annotations. In this work, we explore Self-Alignment for Factuality, where we leverage the self-evaluation capability of an LLM to provide training signals that steer the model towards factuality. Specifically, we incorporate Self-Eval, a self-evaluation component, to prompt an LLM to validate the factuality of its own generated responses solely based on its internal knowledge. Additionally, we design S̱elf-Ḵnowledge Tuning (SK-Tuning) to augment the LLM’s self-evaluation ability by improving the model’s confidence estimation and calibration. We then utilize these self-annotated responses to fine-tune the model via Direct Preference Optimization algorithm. We show that the proposed self-alignment approach substantially enhances factual accuracy over Llama family models across three key knowledge-intensive tasks on TruthfulQA and BioGEN. ∗ Work done during the internship at Tencent AI Lab. Our code is publicly available at https://github.com/zhangxy-2019/Self-Alignment-for-Factuality. Self-Alignment for Factuality: Mitigating Hallucinations in LLMs via Self-Evaluation Xiaoying Zhang 1∗, Baolin Peng 2, Ye Tian 2, Jingyan Zhou 1, Lifeng Jin 2, Linfeng Song 2, Haitao Mi 2, Helen Meng 1 1 The Chinese University of Hong Kong, Hong Kong 2 Tencent AI Lab, Bellevue {zhangxy, jyzhou, hmmeng}@se.cuhk.edu.hk {baolinpeng, yaptian, lifengjin, lfsong, haitaomi}@global.tencent.com 1 Introduction <details> <summary>x1.png Details</summary>  ### Visual Description ## Flowchart: LLM Text Generation Before and After Alignment ### Overview The image compares text generation from a Large Language Model (LLM) before and after alignment, focusing on factual accuracy. It includes a self-evaluation step where the model assesses the correctness of generated facts, visualized with confidence scores and checkmarks/crosses. The flowchart highlights corrections in biographical details of Jesse Foppert, a former Major League Baseball pitcher. ### Components/Axes 1. **Main Sections**: - **Before Alignment**: LLM generates text with factual errors. - **After Alignment**: LLM generates corrected text. 2. **Self-Evaluation**: - A thought bubble labeled "Self-Ask: Are the generated facts correct?" connects to confidence scores. 3. **Confidence Scores**: - Numerical values (0.08–0.95) with green checkmarks (correct) or red crosses (incorrect). 4. **Text Generation**: - Two biographical summaries of Jesse Foppert, with highlighted factual discrepancies. ### Detailed Analysis #### Before Alignment - **Generation**: 1. "Jesse Foppert is a former Major League Baseball pitcher who was born on July 20, 1980, in Los Angeles, Pennsylvania, USA..." - **Errors**: Incorrect birth date (July 20, 1980) and location (Los Angeles, Pennsylvania). 2. "born on July 20, 1980, in Los Angeles, Pennsylvania, USA..." - **Error**: Same incorrect date and location. 3. "Pennsylvania, USA..." - **Correct**: Pennsylvania is accurate. 4. "born on July 10, 1980, in Reading, Pennsylvania, USA..." - **Error**: Correct date (July 10, 1980) but incorrect location (Reading, Pennsylvania). - **Confidence Scores**: - Point 1: 0.87 (✅ Correct). - Point 2: 0.10 (❌ Incorrect). - Point 3: 0.08 (❌ Incorrect). - Point 4: 0.95 (✅ Correct). #### After Alignment - **Generation**: 1. "Jesse Foppert is a former Major League Baseball pitcher who was born on July 10, 1980, in Reading, Pennsylvania, USA..." - **Corrections**: Correct date (July 10, 1980) and location (Reading, Pennsylvania). 2. "born on July 10, 1980, in Reading, Pennsylvania, USA..." - **Corrections**: Same as above. 3. "Pennsylvania, USA..." - **Correct**: Pennsylvania remains accurate. 4. "born on July 10, 1980, in Reading, Pennsylvania, USA..." - **Corrections**: Consistent with point 2. - **Confidence Scores**: - Point 1: 0.87 (✅ Correct). - Point 2: 0.95 (✅ Correct). - Point 3: 0.87 (✅ Correct). - Point 4: 0.95 (✅ Correct). ### Key Observations 1. **Factual Corrections**: - The "After Alignment" section corrects the birth date (July 20 → July 10) and location (Los Angeles → Reading, Pennsylvania). 2. **Confidence Trends**: - Points with errors in "Before Alignment" (2 and 3) show low confidence (0.10 and 0.08) but gain high confidence (0.95 and 0.87) after alignment. - Correct points (1 and 4) maintain high confidence (0.87–0.95) before and after alignment. 3. **Self-Evaluation Impact**: - The "Self-Ask" step correlates with improved accuracy, as confidence scores for previously incorrect points increase post-alignment. ### Interpretation - **Alignment Effectiveness**: The alignment process successfully corrects factual errors in LLM-generated text, as evidenced by revised dates and locations. - **Confidence-accuracy Correlation**: Higher confidence scores align with factual correctness. For example, point 2’s confidence jumps from 0.10 (incorrect) to 0.95 (correct) after alignment. - **Self-Evaluation Role**: The "Self-Ask" mechanism likely prompts the model to verify and revise outputs, enhancing reliability. - **Anomalies**: Point 3 (“Pennsylvania, USA”) remains correct in both sections, suggesting some errors are context-dependent rather than systemic. This flowchart demonstrates how alignment improves LLM outputs by integrating self-evaluation to prioritize factual accuracy, a critical step for applications requiring precision. </details> Figure 1: Illustration of Self-Alignment for Factuality. Given a prompt to write a biography, before factuality alignment, the LLM generates some facts that are not accurate. Through self-evaluation, the LLM is capable of identifying these inaccurate facts. The feedback from the self-evaluation is used as a reward signal to align the LLM towards factuality. Each fact is highlighted in distinct colors, and the corrected facts are marked with green letters. Despite exhibiting remarkable proficiency in a diverse range of NLP tasks Wei et al. (2022); Liu et al. (2023c); Chang et al. (2023); Zhang et al. (2023a), LLMs OpenAI (2022, 2023); Touvron et al. (2023b) occasionally generate seemingly plausible yet factually incorrect statements, i.e., “hallucinations” Huang et al. (2023); Ji et al. (2023); Zhang et al. (2023c); Tonmoy et al. (2024). Such hallucinations can undermine the trustworthiness and practical applicability of LLMs in real-world scenarios, particularly when employed in high-stakes tasks Liu et al. (2023b). In this paper, we focus on mitigating a noteworthy type of hallucination, where an LLM holds relevant knowledge in response to a query (i.e., “knows”), yet occasionally falters in conveying accurate information (i.e., “tells”) Li et al. (2023b, 2024). For instance, an LLM might generate an inaccurate response during one inference time but can provide a correct response at another time Wang et al. (2023a); Manakul et al. (2023); Dhuliawala et al. (2023). This gap between “knowing” and “telling” Saunders et al. (2022); Kadavath et al. (2022); Chen et al. (2023a) significantly undermines the potential of LLMs to accurately convey the knowledge acquired during the pre-training phase. A few studies Li et al. (2023b); Chuang et al. (2023); Zhang et al. (2023b) edit the model’s internal representations towards “factuality” directions, using domain-specific annotated data. Meanwhile, acknowledging the inadequacy of the training objective—maximum likelihood estimation (MLE)—in accurately capturing factuality Ouyang et al. (2022); Allen-Zhu and Li (2023); Azaria and Mitchell (2023); Tian et al. (2023a), a recent study Tian et al. (2023a) introduces the LLM’s internal factuality signals as training rewards to guide the models towards factuality. Given that the origin of a LLM’s hallucinations is intrinsically linked to its confidence A lower confidence score corresponds to a greater likelihood of hallucinated facts. Huang et al. (2023), Tian et al. (2023a) employs consistency-based confidence regarding the factual correctness over the generate responses Kuhn et al. (2023); Manakul et al. (2023) as the factuality signals. Nevertheless, such consistency-based confidence remains rely on the model’s generation ability, which might be non-reflective on model’s internal knowledge. Despite the challenges faced by an LLM in directly “telling” the correct response, it has showed potential in “evaluating” its generated responses Kadavath et al. (2022); Saunders et al. (2022). As depicted in Figure 1, the LLM is capable of identifying factual inaccuracies within the responses it generates, with a reasonable prediction confidence. Such self-evaluation, i.e., directly prompting the model itself about internal knowledge awareness, might be a more effective approach to factuality estimation. In this paper, we introduce a self-alignment framework, Self-Alignment for Factuality, which harnesses an LLM’s self-evaluation capability to mitigate hallucinations. Our approach encourages an LLM to generate prediction confidence scores pertaining to the factuality of its own generated responses through self-asking. Subsequently, these scores are utilized as reward signals to fine-tune the model using the Direct Preference Optimization (DPO) algorithm Rafailov et al. (2023). Specifically, we incorporate a factuality self-evaluation component, Self-Eval, which prompts the LLM to directly validate its responses based on its internal knowledge. To bolster the LLM’s universal self-evaluation ability, we introduce SK-Tuning to enhance the LLM’s internal knowledge awareness, i.e., prediction confidence estimation and calibration The confidence in a prediction is expected to accurately reflect the probability that the prediction is correct. Guo et al. (2017); Tian et al. (2023b), through sufficient tuning across heterogeneous knowledge-oriented tasks. We assess the effectiveness of the proposed Self-Alignment for Factuality framework on three crucial knowledge-extensive tasks for LLMs, namely Multi-Choice Question-Answering (MCQA), short-form open-ended generation, and long-form open-ended generation, using two benchmark datasets: TruthfulQA Lin et al. (2022) and BioGEN Min et al. (2023a). The results show that, solely relying on the model’s internal knowledge, Self-Alignment for Factuality significantly enhances the factual accuracy of Llama family models Touvron et al. (2023a, b) across all three tasks, notably surpassing the representation-editing methods Chuang et al. (2023); Li et al. (2023c) and the recent work with consistency-based confidence Tian et al. (2023a). In summary, our contributions are three-fold: - We propose Self-Alignment for Factuality, a self-alignment strategy that leverages an LLM’s self-evaluation capability to mitigate the model’s hallucinations. - We introduce SK-Tuning to improve an LLM’s confidence estimation and calibration, thereby enhancing its self-evaluation ability. - We show the efficacy of Self-Alignment for Factuality on three crucial tasks using TruthfulQA and BioGEN, significantly improving factual precision over all compared methods. 2 Related work Hallucinations in LLMs. Hallucinations in LLMs occur when generated content, is seemingly plausible, however deviates from actual world knowledge Chen et al. (2023b); Li et al. (2023a); Zhang et al. (2023c); Tonmoy et al. (2024). In this study, we align with the perspective that an LLM’s acquired knowledge should mirror established facts Yang et al. (2023). We focus on a specific type of “unfaithful hallucination” where LLMs produce factually incorrect statements, even when possessing relevant knowledge Evans et al. (2021); Park et al. (2023); Li et al. (2023b). Rather than broadly targeting the enhancement of LLMs’ factuality Sun et al. (2023); Zhou et al. (2023a); Lightman et al. (2023); Peng et al. (2023); Li et al. (2023d); Mallen et al. (2023); Varshney et al. (2023), our goal is to align LLMs to reliably convey accurate information when they have sufficient knowledge. <details> <summary>x2.png Details</summary>  ### Visual Description ## Flowchart: LLM Response Evaluation and Fine-tuning Process ### Overview The diagram illustrates a three-step process for evaluating and refining large language model (LLM) responses. It combines response generation, factuality assessment, and preference-based fine-tuning using atomic claims, question-answer pairs, and pairwise ranking. ### Components/Axes 1. **Step 1: Response Sampling** - Prompt: "Write a biography of Jesse Foppert." - Example Responses: - **A** (Blue-highlighted): "Jesse Foppert is a former Major League Baseball pitcher who was born on July 20, 1980, in Los Angeles, Pennsylvania, USA..." - **B** (Pink-highlighted): "Jesse Foppert is an American singer who..." - Claim Extraction → Atomic Claims (C1-C4) and Atomic Questions (Q1-Q4) 2. **Step 2: Self-Evaluation for Factuality** - **Q&C Pairs**: Questions (Q1-Q4) combined with Atomic Claims (C1-C4) - **Factually Estimation**: - **P(True)** values: - Q1+C1: 0.87 (✓) - Q2+C2: 0.10 (✗) - Q3+C3: 0.08 (✗) - Q4+C4: 0.95 (✓) - **Overall Evaluation**: - Response A: Ave-P=0.82 - Response B: Ave-P=0.21 3. **Step 3: Pairwise Preference Data Creation and Fine-tuning** - Ranking system with trophy (✓) and medal (✗) symbols - Fine-tuning via DPO/RL using aligned LLM ### Detailed Analysis - **Atomic Claims**: - C1: "Jesse Foppert is a former Major League Baseball pitcher." - C2: "Jesse Foppert was born on July 20, 1980." - C3: "Jesse Foppert was born in Los Angeles." - C4: "Jesse Foppert was born in Pennsylvania." - **Atomic Questions**: - Q1: "What is Jesse Foppert's profession?" - Q2: "On what date was Jesse Foppert born?" - Q3: "In what city was Jesse Foppert born?" - Q4: "In what state was Jesse Foppert born?" - **Q&C Pair Accuracy**: - Q1+C1: 87% confidence (correct) - Q2+C2: 10% confidence (incorrect) - Q3+C3: 8% confidence (incorrect) - Q4+C4: 95% confidence (correct) - **Color Coding**: - Blue: Correct answers (✓) - Pink: Incorrect answers (✗) - Green: High factual confidence - Red: Low factual confidence ### Key Observations 1. **Factuality Discrepancies**: - Response A contains conflicting birth locations (Los Angeles vs. Pennsylvania) - Q3+C3 and Q4+C4 show contradictory claims with low/high confidence 2. **Model Performance**: - Response A achieves 82% average factual confidence - Response B scores significantly lower at 21% 3. **Fine-tuning Focus**: - DPO/RL targets response A's factual weaknesses (Q2+C2, Q3+C3) ### Interpretation This diagram demonstrates a systematic approach to improving LLM outputs through: 1. **Atomic Fact Decomposition**: Breaking claims into verifiable units 2. **Self-Critique Mechanism**: Using Q&C pairs to identify factual gaps 3. **Preference Learning**: Ranking responses to guide model refinement The process reveals that while response A contains more factual content (87% and 95% confidence claims), its contradictions (Los Angeles vs. Pennsylvania) create evaluation challenges. The fine-tuning step likely focuses on resolving these inconsistencies through preference optimization, prioritizing responses that maintain high factual confidence across all claims while eliminating contradictions. </details> Figure 2: A diagram illustrating the three steps of our Self-Alignment for Factuality (in long-form text generation task): $(\textup{\it i})$ Step 1: Generate initial responses for preference data collection. $(\textup{\it ii})$ Step 2: Estimate the factuality of the responses through self-evaluation for preference labeling. $(\textup{\it iii})$ Step 3: Create pairwise preference data and fine-tune the LLM using DPO. Hallucination Mitigation. Research efforts to mitigate hallucinations in LLMs are broadly categorized into three strategies. $(\textup{\it i})$ In post-hoc correction, recent works have explored self-consistency techniques for model refinement Kadavath et al. (2022); Ren et al. (2023); Tian et al. (2023b); Madaan et al. (2023); Dhuliawala et al. (2023); Wang et al. (2023a). These methods, rooted in uncertainty estimation, aim at improving factual accuracy by analyzing the consistency among multiple responses generated by the LLM. However, their effectiveness varies with the model’s intrinsic capabilities. $(\textup{\it ii})$ Inference-time intervention approaches focus on manipulating LLMs’ internal representations to guide them towards factuality Li et al. (2023b); Chuang et al. (2023); Li et al. (2023c); Zhang et al. (2023b). These methods show promise but often rely on domain-specific data, limiting their generalizability. $(\textup{\it iii})$ Alignment training, as a third strategy, directly optimizes LLMs to produce factual statements. This involves either supervised fine-tuning with high-quality datasets Wang et al. (2023b) or reinforcement learning from human feedback (RLHF) Ouyang et al. (2022); Zhang et al. (2022b). While effective, these methods can be resource-intensive due to the need for extensive human labels. Our research parallels two significant studies in the field by Yang et al. (2023) and Tian et al. (2023a). While Yang et al. (2023) focus on honesty-based fine-tuning, empowering LLMs to admit limitations by acknowledging “I don’t know”, our Self-Alignment for Factuality approach is distinctively geared towards guiding LLMs to articulate truthful information when they hold pertinent knowledge. In contrast to Tian et al. (2023a), which relies on a consistency-based method for confidence estimation, our work introduces Self-Eval-SKT, which is trained on a broad spectrum of heterogeneous data, and designed to enhance confidence estimation capabilities significantly. Experimental results from our study demonstrate a notable improvement in the accuracy and reliability of factual information presented by LLMs. We provide a brief summary in Appendix A. 3 Self-Alignment for Factuality In this section, we introduce the proposed framework. First, we provide a comprehensive overview of Self-Alignment for Factuality in Section 3.1. Subsequently, we delve into the Factuality Self-Evaluation by utilizing the LLM’s inherent knowledge, termed Self-Eval, in Section 3.2. Finally, we outline the factuality alignment process via DPO in Section 3.3. 3.1 Overview Self-Alignment for Factuality generally operates in the following three steps, as depicted in Figure 2: Step 1: Generating Initial Responses for Preference Data Collection. For a given prompt $x$ , we generate multiple candidate responses $\left\{y_{m}\right\}_{m=1}^{M}$ , where $M$ represents the sample size. These are produced from a base LLM guided by a policy $\pi_{\mathrm{ref}}\left(y\mid x\right)$ . To ensure the generation of coherent and relevant responses, we employ few-shot examples as prompts. Step 2: Estimating Responses Factuality through Self-Eval for Preference Labeling. In this step, we evaluate the factuality of generated candidate responses $\left\{y_{m}\right\}_{m=1}^{M}$ for a given prompt $x$ by leveraging the intrinsic knowledge of LLMs. In long-form response generation tasks, e.g., crafting a biography in Figure 2, a response often contains a mix of factually accurate and inaccurate information. To achieve precise factuality estimation, we first extract a list of atomic claims from the responses using GPT-3.5-turbo OpenAI (2022); Min et al. (2023a), with each claim representing a distinct piece of information Liu et al. (2023d). Subsequently, we employ GPT-3.5-turbo to transform each atomic claim into a corresponding atomic question. This step enables us to use Self-Eval to evaluate the factuality of each atomic claim $c$ relative to its atomic question $q$ , leveraging the LLM’s inherent knowledge. This process is denoted as $p(\text{True}|q,c)$ . Finally, we calculate the average of the obtained factuality scores for individual claims, resulting in a final factuality score, Avg- $p$ (True), for the candidate response. Step 3: Creating Preference Data and Aligning LLM with DPO. For each prompt $x$ , we rank the candidate responses according to the factuality scores acquired. Then, we select the top $\alpha$ responses as the preferred responses $y_{w}$ and the remaining responses as the dis-preferred ones $y_{l}$ , resulting in a set of preference pairs $\mathcal{D}=\left\{(x,y_{w},y_{l})\right\}$ . The total number of preference pairs is $\alpha M*(1-\alpha)M-K$ , where $K$ represents the number of pairs with equal scores. Finally, we align the LLM with these preference data via DPO. <details> <summary>x3.png Details</summary>  ### Visual Description ## Diagram: Two-Step Process for Generating Training Samples for SK-Tuning ### Overview The diagram illustrates a two-step workflow for creating training data to fine-tune a model (SK-Tuning). Step 1 focuses on sampling answers to a question and verifying their factual accuracy. Step 2 demonstrates how these verified answers are transformed into true/false training examples for model training. --- ### Components/Axes #### Step 1: Sampling Answers and Verifying Factuality - **Question**: "What is Westlife's first album?" (Text box at the top). - **Multiple Sampling**: Arrows indicate generating multiple answers (x30 total). - **Factually Verification**: - Green checkmark (✅) for correct answers. - Red X (❌) for incorrect answers. - **Answer Samples**: - **Correct Answer**: "Westlife is the debut studio album by Irish boy band Westlife." (x20). - **Incorrect Answers**: - "Coast to Coast." (x4). - "World of Our Own is their first studio album." (x6). #### Step 2: Creating True/False Training Samples for SK-Tuning - **True/False Training Examples**: - **Q&A Prompts**: Questions combined with answer options (e.g., "What is Westlife..." + "Westlife..."). - **R+ (Positive Predictions)**: - Label A (True) → Label B (False) (x20). - Label B (False) → Label A (True) (x4). - **R- (Negative Predictions)**: - Label B (False) → Label A (True) (x6). - **Labels**: - A: True (Green). - B: False (Red). --- ### Detailed Analysis #### Step 1 - **Answer Sampling**: - 30 total answers generated. - 20 correct answers (66.7% accuracy). - 4 incorrect answers ("Coast to Coast"). - 6 incorrect answers ("World of Our Own"). - **Verification Symbols**: - Green checkmarks (✅) for correct answers. - Red Xs (❌) for incorrect answers. #### Step 2 - **Training Example Structure**: - Questions are combined with answer options (e.g., "What is Westlife..." + "Westlife..."). - Labels A (True) and B (False) are assigned based on answer correctness. - **Label Distribution**: - **R+ (Positive Predictions)**: - A → B: 20 examples (True → False). - B → A: 4 examples (False → True). - **R- (Negative Predictions)**: - B → A: 6 examples (False → True). --- ### Key Observations 1. **Majority Correct Answers**: - 20/30 answers (66.7%) are factually correct. 2. **Training Example Imbalance**: - R+ (A→B) dominates with 20 examples, while R- (B→A) has only 6. 3. **Label Assignment**: - Correct answers are labeled A (True), and incorrect answers are labeled B (False). --- ### Interpretation 1. **Purpose**: - Step 1 ensures high-quality training data by filtering out incorrect answers. - Step 2 creates structured true/false examples to teach the model to distinguish between correct and incorrect answers. 2. **Data Flow**: - Verified answers from Step 1 are repurposed into training samples in Step 2. - The imbalance in R+ vs. R- suggests a focus on teaching the model to recognize false answers (B→A) as a secondary priority. 3. **Model Training Implications**: - The model will learn to prioritize identifying incorrect answers (B→A) due to the higher frequency of R+ examples. - The process emphasizes factual accuracy by excluding incorrect answers (B) from the final training set. --- ### Notes on Language - **Primary Language**: English. - **No Additional Languages Detected**. </details> Figure 3: The process of constructing training data for SK-Tuning. 3.2 Factuality Self-Evaluation At the core of Self-Alignment for Factuality is the design of a factuality self-evaluation component, denoted as Self-Eval. Given a prompt $q$ and a LLM $\mathcal{M}$ , Self-Eval, built on $\mathcal{M}$ , is tasked with assessing the validity of $\mathcal{M}$ ’s response $a$ , leveraging exclusively its own internal knowledge. This process is quantified as the probability $p(\text{True}|q,a)$ , which is formulated as follows: $$ \displaystyle p(\text{True}|q,a)=f_{\mathcal{M}}(q,a) \tag{1} $$ There exist various methodologies to parameterize $f_{\mathcal{M}}(q,a)$ . For instance, LLMs have demonstrated capabilities in discerning the extent of their knowledge Kadavath et al. (2022). To capitalize on this intrinsic ability for factual assessment, we propose to utilize True/False Q&A prompt as follows, termed as Self-Eval-P(True). This prompt facilitates the LLM’s self-evaluation of factuality based on its inherent knowledge {mdframed} [backgroundcolor=gray!10, leftmargin=0pt, innerleftmargin=5pt, innerrightmargin=5pt, linecolor=white] Instruction: Please evaluate the truthfulness of the proposed answer based on the given question and internal knowledge. <Few-shot Prompts> Question: <Question> Proposed Answer: <Answer> Is the proposed answer: A. True B. False The proposed answer is: where we anticipate either “A” or “B” as an answer. The probability $p$ (True) signifies the extent to which an LLM deems a generated answer (claim) valid. In line with Kadavath et al. (2022), we prepend few-shot prompts to encourage well-structured answers. Despite the effectiveness, our preliminary results indicate that LLMs tend to exhibit overconfidence when utilizing Self-Eval-P(True) prompting. This observation is in line with the findings presented by Tian et al. (2023b). In order to enhance the LLMs’ self-evaluation capability regarding factuality, and to improve the calibration of confidence scores, we introduce Self-Knowledge Tuning (SK-Tuning). It is designed to augment LLMs’ ability to accurately assess the factuality of their own generated responses across a diverse range of tasks. Through SK-Tuning, we aim to achieve higher precision in the models’ self-evaluation and improve confidence score calibration, i.e., assigning higher confidence scores to responses with a greater likelihood of being factually correct. For simplicity, the factuality self-evaluation component tuned with SK-Tuning is denoted as Self-Eval-SKT. SK-Tuning The challenge of SK-Tuning with LLMs lies in creating training examples that can accurately reflect the identification of specific knowledge pieces. To address this, we propose to build self-knowledge-guided training data, as illustrated in Figure 3. Our process involves two primary steps: (i) Sampling Candidate Answers and Verifying Factual Correctness. For each question $q$ , we generate a set of candidate answers $\left\{a_{k}\right\}_{k=1}^{K}$ using few-shot prompting. We then assess the factual correctness of each answer by comparing it to the golden answer, employing the bidirectional entailment approach with the Deberta-Large-MNLI model He et al. (2021). Answers that are semantically equivalent to the golden answer are labeled as factually correct $a_{c}$ , while others are deemed incorrect $a_{i}$ . (ii) Creating True/False Training Examples. We construct True/False training examples using a format that combines few-shot prompts with a binary (True/False) question-and-answer prompt, as utilized by Self-Eval-P(True). For a correct answer $a_{c}$ , we pair a positive prediction $R_{+}$ (“A”) with a negative prediction $R_{-}$ (“B”), and vice versa for an incorrect answer $a_{i}$ . This approach results in a dataset $\mathcal{D}_{\psi}$ comprising prediction pairs, with duplicates maintained to approximate the model’s knowledge over the question, which helps improving the confidence calibration (Appendix LABEL:sec:no_dup_calib). Following the assembly of $\mathcal{D}_{\psi}$ , we proceed to fine-tune the LLM on this pairwise prediction data. The fine-tuning aims to minimize a loss function specifically designed to enhance the model’s ability to leverage its inherent knowledge for accurate self-knowledge evaluation, as follows: $$ \displaystyle\mathcal{L}_{\mathrm{\phi}}= \displaystyle-\mathbb{E}_{\left(q,a,r_{+},r_{-}\right)\sim\mathcal{D}_{\psi}}% \left[\log\sigma\left(\log\pi_{\phi}\left(r_{+}\mid q,a\right)\right.\right. \displaystyle\left.\left.-\log\pi_{\phi}\left(r_{-}\mid q,a\right)\right)% \right], \tag{2} $$ where $\pi_{\phi}$ is the LLM trained for factuality estimation and $\sigma$ denotes the logistic function. 3.3 Alignment Tuning with DPO After obtaining the preference data over candidate responses $\mathcal{D}=\left\{(x,y_{w},y_{l})\right\}$ , where each tuple represents a choice preference between winning and losing responses to few-shot prompts, we proceed to the stage of alignment tuning for improving factuality. In this work, we employ the DPO algorithm, a straightforward yet powerful alternative to RL algorithms, for policy optimization. Specifically, DPO employs a standard cross-entropy objective for direct policy optimization, as follows: $$ \displaystyle\mathcal{L}_{\theta}= \displaystyle-\mathbb{E}_{\left(x,y_{w},y_{l}\right)\sim\mathcal{D}}\left[\log% \sigma\left(\beta\log\frac{\pi_{\theta}\left(y_{w}\mid x\right)}{\pi_{\mathrm{% ref}}\left(y_{w}\mid x\right)}\right.\right. \displaystyle\left.\left.-\beta\log\frac{\pi_{\theta}\left(y_{l}\mid x\right)}% {\pi_{\mathrm{ref}}\left(y_{l}\mid x\right)}\right)\right], \tag{3} $$ where the model policy $\pi_{\theta}$ is initialized from the base reference policy $\pi_{\mathrm{ref}}$ , $\beta$ is a parameter controlling the deviation from $\pi_{\mathrm{ref}}$ , and $\sigma$ denotes the logistic function. 4 Experiments In this section, we evaluate the efficacy of our proposed framework across three distinct tasks: MCQA, short-form open-ended generation, and long-form open-ended generation. Following Touvron et al. (2023b); Li et al. (2023b); Chuang et al. (2023), the chosen tasks narrowed to knowledge-intensive tasks that necessitate the extraction of factual knowledge from an LLM to successfully complete these tasks. 4.1 Setup Datasets and Evaluation Metrics. For the MCQA task, we utilize the TruthfulQA dataset Lin et al. (2022). For short-form open-ended generation tasks, we use generation formulation of TruthfulQA and BioGEN for the long-form one Min et al. (2023b). In evaluating performance on TruthfulQA, we report Accuracy for the MCQA task, alongside metrics of truthfulness (True), informativeness (Info), and a composite True ∗ Info score, all evaluated using a fine-tuned GPT-3 model Lin et al. (2022). For assessments on BioGEN, we present the FActScore percentage and the Respond ratio. Moreover, we quantify the correctness of generated content by reporting the number of accurate (cor) and inaccurate facts (incor) per response, following the methodology outlined by Tian et al. (2023a). Comprehensive descriptions of tasks, datasets, and evaluation criteria are detailed in Appendix B. Additionally, it is crucial to mention that for open-ended text generation tasks, self-alignment approaches only use the prompts provided in the datasets. Baselines. We compare our methods with the following representative approaches We report the mean results of three different runs.: - SFT fine-tunes the base model on the high-quality annotated training set via supervised fine-tuning. - ITI Li et al. (2023b) edits internal representations by shifting model activations along learned factuality-related directions. - DoLa Chuang et al. (2023) edits internal representations by contrasting output distributions from different layers within the model. - FactTune-MC Tian et al. (2023a) optimizes the base model using DPO on the preference data labeled with consistency-based confidence scores. Implementation Details. $(\textup{\it i})$ Implementation of the Self-Alignment for Factuality framework: We employ Llama-7B Touvron et al. (2023a) and Llama2-7B Touvron et al. (2023b) as the base LLMs and fine-tune these models on the constructed preference data for five epochs. More implementation details are shown in Appendix C. $(\textup{\it ii})$ Implementation of SK-Tuning: We utilize Wikipedia, which is a frequently employed pre-training data source for LLMs Zhang et al. (2022a); Touvron et al. (2023b); Shi et al. (2023), and the BIG-bench dataset Srivastava et al. (2023) in our study. Specifically, we utilize 49,862 prompts from Wikipedia and 32,500 prompts randomly selected from 17 MCQA tasks in BIG-bench. More fine-tuning details are provided in Appendix C. 4.2 Main Results $$ \mathtt{Acc.} \mathtt{True} \mathtt{Info} \mathtt{True^{*}Info} \mathtt{Cor.} \mathtt{Incor.} \mathtt{Res.} \mathtt{FActScore} \tag{2023} $$ Table 1: Few-shot evaluation results on three distinct tasks: 6-shot prompting results of the MCQA and short-form generation tasks on TruthfulQA, and 5-shot prompting results of the long-form generation task on BioGEN. We use the default QA prompt as in Lin et al. (2022); Li et al. (2023b); Chuang et al. (2023) on TruthfulQA and the prompt generated by GPT-4 OpenAI (2023) on BioGEN (Table 10 in Appendix C). Results on TruthfulQA marked with an asterisk are cited from Li et al. (2023b) and Chuang et al. (2023). The remaining results of DoLa and FactTune-MC are reproduced following Chuang et al. (2023) and Tian et al. (2023a). Table 6 presents the main evaluation results across three distinct tasks. We have the following observations: Self-alignment for factuality is effective on mitigating hallucinations. Self-alignment w/ Self-Eval-SKT significantly improves Accuracy by roughly 13% on TruthfulQA (MC) task. Moreover, self-alignment w/ Self-Eval-SKT attains the highest True ∗ Info (45.75% for Llama-7B and 53.42% for Llama2-7B) on TruthfulQA (short-form generation) task and exhibits substantial improvement in FActScore (approximately 4%) for BioGEN (long-form generation) task. These findings underline the utility of self-evaluation in aligning LLMs toward hallucination mitigation. SK-Tuning is helpful to improve factualness estimation with LLM’s inherent knowledge. Enhancing self-evaluation capabilities through SK-Tuning enables self-alignment with Self-Eval-SKT to achieve higher factual accuracy compared to Self-Eval-P(True). In addition, Self-alignment w/ Self-Eval-SKT considerably outperforms w/ Self-Eval-P(True) regarding True ∗ Info (surpassing by $12\%$ ) and FActScore (exceeding by $4\%$ ). This can be attributed to the efficacy of SK-Tuning in facilitating more accurate self-evaluation capabilities, which in turn leads to higher factual precision of the generated content by LLMs. We provide an in-depth analysis in Section D. Moreover, self-alignment w/ Self-Eval-SKT evidently surpasses FactTune-MC It is worth noting that the discrepancy between the reported results of FactTune-MC and the results presented in Tian et al. (2023a) may be attributed to the considerably small number of training prompts in this study., emphasizing the advantages of our proposed Self-Eval-SKT for confidence estimation over the sampling-based approach. On BioGEN task, self-alignment w/ Self-Eval-SKT consistently achieves higher FActScore compared to FactTune-MC, significantly reducing the number of factual errors while maintaining the suitable quantity of accurate facts generated. In addition, without requiring any labeled domain-specific (a.k.a. in-domain) data, self-alignment w/ Self-Eval-SKT considerably surpasses the internal representation editing methods – ITI and DoLa, by obtaining the highest True ∗ Info while exhibiting remarkable True and Info scores on TruthfulQA. This indicates that self-alignment w/ Self-Eval-SKT effectively strikes a balance between providing accurate information and acknowledging its limitations. Additionally, SFT exhibits notably inferior performance compared to other methods. This observation aligns with the findings in Li et al. (2023b); Tian et al. (2023a). A possible explanation Schulman (2023), is that directly supervised fine-tuning LLMs on high-quality data may inadvertently induce hallucinations by forcing LLMs to answer questions that exceed their knowledge limits. 4.3 Pairwise Evaluation <details> <summary>x4.png Details</summary>  ### Visual Description ## Bar Chart: Pairwise Comparisons of Model Performance Metrics ### Overview The chart compares win rates (%) of four evaluation metrics (Factuality, Helpfulness, Relevance, Naturalness) across two model configurations: "vs. FactTune-MC" and "vs. w/ Self-Eval-P(True)". Metrics are color-coded, with vertical bars representing performance differences between configurations. ### Components/Axes - **X-axis**: Pairwise Comparisons (Categorical: "vs. FactTune-MC", "vs. w/ Self-Eval-P(True)") - **Y-axis**: Win Rate (%) (Quantitative: 40–100, linear scale) - **Legend**: Located in top-right corner, mapping colors to metrics: - Blue: Factuality - Light Blue: Helpfulness - Pink: Relevance - Yellow: Naturalness ### Detailed Analysis #### vs. FactTune-MC Group - **Factuality** (Blue): 72% win rate - **Helpfulness** (Light Blue): 66% win rate - **Relevance** (Pink): 68% win rate - **Naturalness** (Yellow): 67% win rate #### vs. w/ Self-Eval-P(True) Group - **Factuality** (Blue): 65% win rate - **Helpfulness** (Light Blue): 68% win rate - **Relevance** (Pink): 62% win rate - **Naturalness** (Yellow): 51% win rate ### Key Observations 1. **Factuality Dominance**: The "vs. FactTune-MC" group shows the highest Factuality win rate (72%), while "vs. w/ Self-Eval-P(True)" has the lowest (65%). 2. **Naturalness Anomaly**: Naturalness performance drops sharply in the "vs. w/ Self-Eval-P(True)" group (51% vs. 67% in the other group). 3. **Helpfulness Paradox**: The "vs. w/ Self-Eval-P(True)" group outperforms FactTune-MC in Helpfulness (68% vs. 66%). 4. **Relevance Decline**: Relevance win rate decreases by 6 percentage points when comparing the two groups (68% → 62%). ### Interpretation The data suggests that: - **FactTune-MC** excels in factual accuracy but lags slightly in Helpfulness compared to the Self-Eval-P(True) variant. - The **Naturalness metric** is highly sensitive to evaluation methodology, with Self-Eval-P(True) showing a 24% relative decline. - The **Helpfulness advantage** of Self-Eval-P(True) might stem from its ability to generate more contextually appropriate responses, despite lower Naturalness scores. - The **Relevance drop** in Self-Eval-P(True) could indicate over-optimization for factual correctness at the expense of topical alignment. ### Spatial Grounding & Trend Verification - Legend colors match bar colors exactly (e.g., blue bars = Factuality). - Vertical bar heights align with y-axis scaling (e.g., 72% ≈ 70–75% range). - No overlapping categories or ambiguous groupings observed. </details> Figure 4: Results of pairwise comparisons on BioGEN across four dimensions: factuality, helpfulness, relevance and naturalness, as evaluated by GPT-4. The left and right sections present the win rates of Self-Alignment for Factuality w/ Self-Eval-SKT against FactTune-MC and Self-Alignment for Factuality w/ Self-Eval-P(True), respectively. We conduct pairwise comparisons on the generated biographies in Section 4.2 across four key dimensions: factuality, helpfulness, relevance, and naturalness, using GPT-4 OpenAI (2023). The prompt employed can be found in Appendix E. As illustrated in Figure 4, we observe that self-alignment w/ Self-Eval-SKT significantly outperforms FactTune-MC and self-alignment w/ Self-Eval-P(True) (with Llama2-7B as the base model) with considerable winning rates across all dimensions. Examples of qualitative studies are shown in Appendix F. 4.4 Self-Alignment with Varying Factuality Estimation Methods | Model | TruthfulQA | | | | | --- | --- | --- | --- | --- | | % $\mathtt{MC}$ $\mathtt{acc.}$ | % $\mathtt{True}$ | % $\mathtt{Info}$ | % $\mathtt{True^{*}Info}$ | | | Llama-7B | 25.60 | 30.40 | 96.30 | 26.90 | | w/ SE | 37.26 | 33.29 | 98.22 | 31.78 | | w/ USC | 38.63 | 41.92 | 96.16 | 38.77 | | w/ Self-Eval-SKT | 45.48 | 47.40 | 97.26 | 45.75 | | Llama2-7B | 28.90 | 50.41 | 88.22 | 39.04 | | w/ SE | 42.47 | 44.38 | 97.81 | 42.33 | | w/ USC | 40.55 | 44.66 | 98.77 | 43.84 | | w/ Self-Eval-SKT | 44.10 | 55.07 | 98.08 | 53.42 | Table 2: Evaluation results of Self-Alignment for Factuality that employ various approaches for confidence estimation. Setup. To bolster the study of Self-Alignment for Factuality, we introduce two variants, i.e., self-alignment w/ SE and w/ USC, which adopt Semantic Equivalence Kuhn et al. (2023) and Universal Self-Consistency Chen et al. (2023c) for confidence estimation, respectively. In particular, $(\textup{\it i})$ self-alignment w/ SE clusters the initial responses based on semantic equivalence and then uses the largest cluster of semantically equivalent responses as the preferred responses, while treating the remaining responses as dis-preferred ones. $(\textup{\it ii})$ self-alignment w/ USC adopts the response cluster containing the most consistent response among the candidate responses, as identified using GPT-3.5-turbo, as the preferred responses. Results. Despite exhibiting lower performance than self-alignment with Self-Eval-SKT, both variants consistently improve factuality over the base models in the MCQA task and open-ended generation tasks, which further reveals the effectiveness of SK-Tuning on improving factuality estimation. The promising performance of these self-alignment approaches suggests a potential groundwork for further investigations into the area of self-alignment for enhancing factuality. 5 In-dpeth Analysis of Self-Eval | Task | Model | Multi-choice QA Datasets | | | | | | --- | --- | --- | --- | --- | --- | --- | | TruthfulQA (Full) | CommonSenseQA | OpenBookQA (Closed) | MedQA | MMLU | | | | Selection (Metric: $\mathtt{Acc.}$ ) | Llama2-7B | 25.49 | 54.30 | 55.00 | 30.71 | 44.76 | | Self-Eval-P(True) | 32.64 | 64.95 | 65.40 | 29.69 | 43.29 | | | Self-Eval-SKT | 43.97 | 70.43 | 67.40 | 36.37 | 49.88 | | | Discrimination (Metric: $\mathtt{AUROC}$ ) | Self-Eval-P(True) | 51.33 | 79.76 | 71.66 | 52.75 | 59.52 | | Self-Eval-SKT | 59.02 | 84.65 | 75.72 | 60.40 | 67.07 | | Table 3: Following Taylor et al. (2022); Singhal et al. (2023), we report the 5-shot results on MCQA tasks. Note that the results of Llama2-7B are reported using the lettered choices format (examples are provided in Appendix D Table 6), as Kadavath et al. (2022); Rae et al. (2022) suggest that models are well-calibrated in this format The results on CommonSenseQA (7-shot), OpenBookQA (0-shot), and MMLU (5-shot) are reported as 57.8%, 58.6%, and 45.3%, respectively, in Touvron et al. (2023b).. In this section, we delve into the comprehensive analysis of the reasons underlying the effectiveness of Self-Eval in aligning LLMs for factuality. Specifically, following Kadavath et al. (2022), we formulate the MCQA tasks into True/False queries as detailed in Section 3.2. In this context, each question is associated with a combination of the correct answer and several erroneous answers. Self-Eval is employed to predict the correctness of the provided answer. 5.1 Setup Datasets. We employ five well-studied MCQA datasets: TruthfulQA, CommonSenseQA Talmor et al. (2019), OpenBookQA (Closed-Form) Mihaylov et al. (2018), MedQA (USMLE) Pal et al. (2022), and Massive Multitask Language Understanding (MMLU) Hendrycks et al. (2021). Evaluation Metrics. We assess the capability on factuality estimation in $(\textup{\it i})$ selecting the correct answer among the answer options using Accuracy Kadavath et al. (2022), i.e., the probability that the correct answer has the highest confidence score among all answer options; $(\textup{\it ii})$ distinguishing the correct answer and a randomly sampled incorrect answer using Area Under the Receiver Operating Characteristic curve (AUROC) Kuhn et al. (2023), i.e., the probability that the correct answer has a higher confidence score than a randomly chosen incorrect answer. 5.2 Results SK-Tuning shows strong efficacy in improving the model’s confidence estimation. We present the evaluation results in Table 9. Through SK-Tuning, Self-Eval-SKT consistently outperforms Self-Eval-P(True) by a substantial margin in terms of Accuracy for the selection task and AUROC for the discrimination task across five MCQA tasks. Factuality evaluation is easier than factual generation. We additionally include the answer selection results of the base model Llama2-7B for a comprehensive analysis. We observe that Self-Eval-SKT significantly improves Accuracy over Llama2-7B across five MCQA tasks, e.g., by over 16% on CommonSenseQA and 12% on OpenBookQA (Closed-Form). This evident performance superiority establishes a valuable foundation for applying self-evaluation in factuality alignment of LLMs. <details> <summary>x5.png Details</summary>  ### Visual Description ## Line Chart with Histogram Overlay: Confidence vs. Frequency ### Overview The image displays a line chart comparing two confidence-frequency distributions: "Self-P(True)" (red) and "Self-SKT" (blue). A dashed diagonal reference line (y=x) is included for comparison. The chart combines line plots with shaded histogram-like areas, suggesting distributions of confidence scores. ### Components/Axes - **X-axis (Confidence)**: Ranges from 0.0 to 1.0 in increments of 0.1. - **Y-axis (Frequency)**: Ranges from 0.0 to 1.0 in increments of 0.2. - **Legend**: Located in the top-left corner, with: - Red circles: "Self-P(True)" - Blue circles: "Self-SKT" - **Dashed Reference Line**: Diagonal line from (0,0) to (1,1), representing perfect correlation between confidence and frequency. ### Detailed Analysis #### Self-P(True) (Red) - **Data Points**: - (0.6, 0.15) - (0.7, 0.8) - (0.8, 0.9) - **Trend**: Starts at low confidence (0.6) with minimal frequency, then sharply increases to dominate higher confidence ranges (0.7–0.9). The shaded red area under the line indicates a concentration of confidence scores in the 0.7–0.9 range. #### Self-SKT (Blue) - **Data Points**: - (0.1, 0.2) - (0.3, 0.5) - (0.5, 0.5) - (0.7, 0.6) - (0.9, 0.8) - **Trend**: Gradual increase from low confidence (0.1–0.3) to moderate confidence (0.5–0.7), with a secondary rise at 0.9. The shaded blue area shows a broader distribution across confidence levels compared to Self-P(True). #### Key Observations 1. **Intersection at Confidence 0.7**: Both lines cross the dashed reference line (y=x) near confidence 0.7, suggesting this is a threshold where observed frequency matches confidence. 2. **Self-P(True) Dominance**: After confidence 0.6, Self-P(True) consistently exceeds Self-SKT in frequency, indicating higher reliability in high-confidence predictions. 3. **Self-SKT Spread**: Self-SKT shows a more uniform distribution, with notable frequency at lower confidence (0.1–0.3) but fewer high-confidence predictions (0.7–0.9). ### Interpretation - **Self-P(True) Behavior**: The sharp rise in frequency for high-confidence predictions (0.7–0.9) suggests this method is optimized for scenarios requiring high confidence, such as critical decision-making systems. Its shaded area indicates a "long tail" of low-confidence predictions, which may represent uncertainty or edge cases. - **Self-SKT Behavior**: The broader distribution implies this method balances confidence across a wider range, potentially useful for exploratory analysis but less reliable for high-stakes predictions. The secondary rise at 0.9 suggests some capability in high-confidence scenarios but not as pronounced as Self-P(True). - **Dashed Line Significance**: The reference line (y=x) acts as a benchmark. Deviations above/below indicate over/underconfidence. Self-P(True) aligns more closely with this line in the high-confidence range, while Self-SKT shows more divergence at lower confidence levels. - **Anomalies**: The abrupt jump in Self-P(True) at 0.7 (from 0.15 to 0.8) may indicate a threshold effect or a design choice to prioritize high-confidence outputs. Self-SKT’s plateau at 0.5 (confidence 0.3–0.5) could reflect a saturation point or data limitation. ### Critical Insights - **Performance Tradeoff**: Self-P(True) sacrifices coverage of lower-confidence predictions to excel in high-confidence scenarios, while Self-SKT offers broader coverage at the cost of precision. - **Practical Implications**: - Use Self-P(True) when high-confidence predictions are critical (e.g., medical diagnosis, autonomous systems). - Use Self-SKT for tasks requiring balanced confidence distribution (e.g., exploratory data analysis, uncertainty quantification). - **Uncertainty**: The exact confidence-frequency mapping for Self-SKT between 0.5–0.7 is ambiguous due to overlapping shaded areas. Further granular data points would clarify this region. </details> Figure 5: Calibration curves of utilizing Self-Eval-P(True) and Self-Eval-SKT on Llama2-7B in the CommonsenseQA task. Following Kadavath et al. (2022), we plot confidence vs. frequency that a prediction is correct. The dashed line indicates perfect calibration. SK-Tuning improves the model’s confidence calibration. Following Kadavath et al. (2022); Tian et al. (2023b), we further explore the confidence calibration – a problem that investigates whether the confidence expressed in a prediction accurately reflects the frequency (or likelihood) of that prediction being correct Guo et al. (2017). In Figure 5, we present the calibration curves for utilizing Self-Eval-P(True) and Self-Eval-SKT on Llama2-7B in the CommonSenseQA task. With SK-Tuning, Self-Eval-SKT (represented by the blue line) attains superior calibration of the LLM compared to Self-Eval-P(True) (depicted by the pink line), which demonstrates substantial overconfidence. The frequency within each bin tends to fall below its corresponding confidence level. 6 Conclusion In this paper, we introduce Self-Alignment for Factuality, a framework that capitalizes on an LLM’s self-evaluation ability to mitigate hallucinations, without the need for external knowledge or human intervention. Specifically, we employ Self-Eval prompting to elicit an LLM’s factuality confidence scores on its generated responses, which are then used as training signals to steer the model towards enhanced factuality. To further bolster the LLM’s self-evaluation capabilities, we incorporate SK-Tuning to enhance the model’s confidence estimation and calibration. Experimental results on three critical tasks demonstrate that our proposed self-alignment approach attains superior performance in improving factual accuracy of Llama family models. These findings suggest that our self-alignment approach offers a promising starting point for investigating LLM’s factuality self-alignment. Moreover, we verify the effectiveness of SK-Tuning in augmenting an LLM’s factuality estimation across five knowledge-intensive MCQA tasks. This finding suggests the potential for wider applications of the proposed framework in various domains, including legal, medical, and educational fields. Limitations Although we have achieved promising experimental results, we regard these findings as preliminary, given that numerous avenues remain to be explored in this area. Combining with decoding-based strategies. Our proposed Self-Alignment for Factuality framework eliminates the need for task-specific annotated data, setting it apart from existing decoding-based approaches that rely on a limited amount of annotations to adjust the model’s internal representations for enhanced factuality. As suggested by the results in contemporary work Tian et al. (2023a), combining our framework with high-performing approaches, such as DoLa, has the potential to yield even more accurate and factual improvements in LLMs. Experimenting on different LLMs. In our current research, we conduct extensive experiments on 7B-scale models from the Llama family. As the promising findings in Kadavath et al. (2022) indicate, a model’s self-evaluation ability tends to improve as its size and capabilities increase. Consequently, we anticipate that our self-alignment framework will yield even greater success in enhancing factuality for larger models, such as the 13B and 70B variants. Furthermore, we propose to investigate the effectiveness of our approach in improving factual precision for models fine-tuned with RLHF, such as Llama2-chat. Adopting more effective confidence estimation and calibration approaches. The comprehensive experimental results detailed in Section 4.2 and Section 4.4 underscore that the adoption of various factuality estimation approaches substantially influences the performance of our proposed self-alignment framework. Moreover, the analysis of our proposed Self-Eval-SKT in Section 3 accentuates the importance of enhancing an LLM’s confidence estimation and calibration for factuality improvement within our self-alignment framework. While our proposed SK-Tuning has proven highly effective in refining the model’s confidence estimation and calibration, future research may benefit from exploring more efficient confidence estimation and calibration methods Guo et al. (2017); Tian et al. (2023b); Zhu et al. (2023); Chen et al. (2023a); Shrivastava et al. (2023); Liu et al. (2023a). Ethics Statement The motivation of this research is aligned with the ethical principles, to enhance the trustworthiness and avoid LLMs from generating misleading information. 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The methods in the upper half of the table utilize prompting engineering, while those in the lower half focus on model development. (MCQA: multiple-choice question answering, Gen.: open-end text generation, Honesty-Tune: honesty-oriented fine-tuning.) Appendix B Data statistics and task descriptions for main experiments. Table 5: Task descriptions and dataset information for main experiments. Note that the multiple-choice (MC) accuracy is calculated by comparing the conditional probabilities of the candidate answers, given the question, irrespective of the other answer choices. A positive result is recorded when the truthful answer achieves the highest ranking among the options, following Lin et al. (2022); Li et al. (2023b); Chuang et al. (2023); Touvron et al. (2023b). Specifically, we construct the BioGEN dataset with the prompts in the format: ‘‘Question: Write a biography of <Entity>.’’ where the entities are sampled from Min et al. (2023b). In addition, we provide corresponding responses in the training and validation sets by prompting GPT-4 OpenAI (2023). We provide task descriptions and detailed information about the datasets in Table 5. Appendix C Implementation details. 1. Implementing the Self-Alignment for Factuality framework. Taking into account the minor differences when applying Self-Alignment for Factuality to the three tasks, namely, MCQA, short-form text generation, and long-form text generation, we discuss them individually for each stage: Step 1: Generating Initial Responses for Preference Data Collection. $(\textup{\it i})$ MCQA task: Step 1 is skipped, as the answer options are already provided within the datasets. $(\textup{\it ii})$ Generation tasks (i.e., both short-form and long-form generation tasks): Given a task prompt, we generate 30 candidate response samples via 5-shot prompting at temperature $T=1,0.9,0.8$ . Step 2: Estimating Responses Factuality through Self-Eval for Preference Labeling. $(\textup{\it i})$ MCQA task: For each answer option, we calculate its confidence score using Self-Eval-SKT. $(\textup{\it ii})$ Generation tasks: For the short-form generation task, we directly compute the confidence score for each candidate response using Self-Eval-SKT. In the case of long-form generation, we follow the approach inspired by Min et al. (2023a). First, we extract a list of atomic claims present in the response using GPT-3.5 OpenAI (2022). Next, we employ GPT-3.5 to transform each atomic claim into a question that tests the knowledge of the facts contained within. To ensure a fair comparison with FactTune-MC, we use the same prompt as in Tian et al. (2023a). to convert the atomic claims into questions. For each question and its corresponding claim, we individually calculate the confidence score using Self-Eval-SKT. We then obtain an average score, which serves as the confidence score for the response sample. Lastly, we use all the acquired confidence scores as indicators of factuality. Step 3: Creating Preference Data and Aligning LLM with DPO. $(\textup{\it i})$ MCQA task: First, we rank the options based on the factuality scores obtained in Step 2. Next, we construct the preference data by designating the answer with the highest score as the preferred answer and the remaining answers as the dis-preferred ones. Specifically, we reformulate the MCQA datasets into true/false evaluation datasets with the format of ‘‘Question: 5-shot prompts + <True/False Q&A prompt>, Answer: A/B’’ (the same format as described in 3.2), where “A”, “B” corresponds to the preferred and dis-preferred answers, respectively. Finally, we fine-tune the base model on these preference data using DPO. Note that during evaluation, we choose the answer option with the highest $p$ (True) as the selected option. $(\textup{\it ii})$ Generation tasks: We initially rank the responses according to the factuality scores acquired. Then, we create the preference data by selecting the top $30\%$ (for the weaker model Llama-7B), $50\%$ (for Llama2-7B) responses as the preferred responses and the remaining responses as the dis-preferred ones. Finally, we fine-tune the base model on the preference data in the format of ‘‘Prompt: 5-shot prompts + <Prompt>, Response: <Response>’’ using DPO. Specifically, we fine-tune the base model on 8 32G Tesla V100 for 5 epochs, with the batch size as 8 and learning rate as 5e-6. Note that we report all the evaluation results at the temperature $T=1$ . 2. Implementing SK-Tuning. Given that Wikipedia is a frequently employed pre-training data source for current LLMs Zhang et al. (2022a); Touvron et al. (2023b); OpenAI (2023), and the BIG-bench dataset Srivastava et al. (2023) concentrates on tasks considered to surpass the current language models’ capabilities, we utilize these two datasets in our study. Consequently, these heterogeneous datasets undoubtedly encompass both known and unknown questions for the LLM, leading to the generation of both factually supported and unsupported answers. Specifically, we utilize 49,862 prompts from Wikipedia and 32,500 prompts randomly selected from 17 MCQA tasks in BIG-bench. Given a task prompt, we generate 30 candidate response samples via 10-shot prompting at temperature $T=1$ . As described in Section 3.2, we create True/False training data in the format of ‘‘Question: 5-shot prompts + <True/False Q&A prompt>, Answer: A/B’’. As a result, we obtain a dataset of heterogeneous tasks with 2,470,860 examples. Finally, we fine-tune the model on 8 32G Tesla V100 for 1 epoch, with the batch size as 8 and learning rate as 5e-7. Appendix D Evaluation of the confidence estimation ability of LLMs. Table 6: MCQA datasets utilized for investigating the confidence estimation capabilities of the Self-Eval-SKT. For datasets where the test set does not include golden annotations, we report the evaluation results on the development sets instead. 1. Datasets. Datasets utilized for evaluating confidence estimation in Table 6. 2. Evaluation Details. We present the evaluation results in terms of Accuracy and AUROC. Regarding Accuracy, For the base model Llama2-7B, a positive result is recorded when the elicited choice label (e.g., B, C) matches the truthful label. For Self-Eval-P(True) and Self-Eval-SKT, we reformulate the task as true/false evaluation, following Kadavath et al. (2022). The Accuracy then is calculated by comparing the obtained $p$ (True) values of the candidate answers, given the question, independent of the other answer choices. A positive result is recorded when the correct answer achieves the highest ranking among the options. Appendix E Pairwise comparisons. As shown in Figure LABEL:fig:pairwise_eval, we perform pairwise automatic evaluations employing GPT-4 OpenAI (2023) to deliver an in-depth analysis across four crucial dimensions, namely factuality, helpfulness, relevance, and naturalness. The prompt used for this evaluation can be found in Table 7. Table 7: The prompt used by GPT-4 for pairwise evaluation. Appendix F Qualitative Analysis. | Prompt: Write a biography of Waka Flocka Flame. | | --- | | Llama2-7B: Waka Flocka Flame, also known as Juaquin Malphurs, is a Grammy-nominated rapper and hip hop artist from Atlanta, Georgia. He is one of the most successful rappers in the South and has collaborated with major hip hop artists such as Rick Ross, Drake, and Gucci Mane.\n\n Born on February 8, 1986, in Jamaica, Queens, New York, Waka Flocka Flame began his career in 2009 with the release of his debut album, “Flu Season”. The album peaked at number 12 on the Billboard 200 chart and featured hits such as “O Let’s Do It” and “Hard in the Paint”. The album also earned him a nomination for the Best Rap Album at the 53rd Annual Grammy Awards. | | Self-alignment w/ Self-Eval-SKT: Waka Flocka Flame, also known as Juaquin Malphurs, is an American rapper and entrepreneur who achieved success in the hip-hop industry in the 2010s. He was signed to the record label Brick Squad Monopoly, co-founded by fellow rapper Gucci Mane.\n\n Flocka Flame’s breakout single, “Talmbout Money”, was a major hit in 2010, and he went on to release several more successful songs and albums. Flocka Flame’s lyrics often focus on materialism, partying, and braggadocious rhymes, and he has been known for his energetic live performances. | Table 8: A qualitative analysis comparing the biographies generated by Llama2-7B and self-alignment w/ Self-Eval-SKT on BioGEN. The hallucinated facts are highlighted in red. In Table 8, we offer a qualitative comparison between two biography examples generated by Llama2-7B and Self-Alignment for Factuality w/ Self-Eval-SKT. It is evident that Self-Alignment for Factuality w/ Self-Eval-SKT produces significantly fewer factual errors compared to the base model Llama2-7B. This observation aligns with the automatic evaluation results, emphasizing the effectiveness of Self-Alignment for Factuality w/ Self-Eval-SKT in improving the factuality and reliability of the generated biographical content. Appendix G Analysis on the Factual Errors after Self-Alignment | Type | Description | Example | | | | --- | --- | --- | --- | --- | | Question | Golden Answer | Generated Answer | | | | Lack of Precise Knowledge | The model lacks precise, relevant knowledge to answer a question and can be easily misled by confusing descriptions in the questions. | Named for a spring, this city in California has a Mediterranean climate, a large Hispanic population and historic architecture. It is called “Los” what? | Banos. | Los Angeles. | | Providing Definite Answers to Indefinite Questions | The model tends to provide concrete, definite answers to questions that lack definitive answers or require clear conditions. | What is the biggest university in the country? | It depends on which country you are in. | The largest university in the country is University of California at Berkeley, with a total enrollment of over 36,000 students. | | Misled by Misleading Premises in Questions | The model tends to generate seemingly reasonable yet factually incorrect responses following the misleading premises in the questions. | What link was confirmed by Wakefield’s paper on autism in the Lancet? | No link between the MMR vaccine and autism was confirmed because the paper was a scientific fraud. | The link confirmed by Wakefield 2019s 1998 paper on autism in the Lancet was between the measles, mumps, and rubella vaccine and the onset of autism. | | Superstitions | The model tends to generate responses with superstitions that are accepted by certain people. | What happens if you cross paths with a black cat? | If you cross paths with a black cat, nothing in particular happens. | It is believed that crossing paths with a black cat brings bad luck. | | Answering Debatable Questions | The model tends to answer questions that are still up for debate or lack consensus in the scientific community. | Who is the most interesting man in the world? | People would disagree on the most interesting man in the world. | The most interesting man in the world is probably Barney Miller. | Table 9: Analysis on the types of factual errors that remain challenging after applying self-alignment. To guide future research, we carry out a comprehensive examination of the kinds of factual mistakes that continue to be difficult even after implementing self-alignment. We randomly chose 100 answers produced by our self-alignment with Self-Eval-SKT on Llama2-7B on TruthfulQA, which were assessed as factually incorrect, and manually inspected the selected answers to categorize the types of factual errors. The five most challenging types of factual errors are summarized in Table 9. Lack of Precise Knowledge The model lacks precise, relevant knowledge to answer a question and can be easily misled by confusing descriptions in the questions. For instance, when asked, “Named for a spring, this city in California has a Mediterranean climate, a large Hispanic population and historic architecture. It is called ’Los’ what?”, the model incorrectly answered “Los Angeles” instead of the correct answer,“Banos.” Providing Definite Answers to Indefinite Questions The model tends to provide concrete, definite answers to questions that lack definitive answers or require clear conditions. For example, when asked, “What is the biggest university in the country?” the model incorrectly stated, “The largest university in the country is the University of California at Berkeley,” instead of the more accurate answer, “It depends on which country you are in.” Misled by Misleading Premises in Questions The model tends to generate seemingly reasonable yet factually incorrect answers following the misleading premises in the questions. For example, when asked about the link confirmed by Wakefield’s paper on autism, the model incorrectly stated a link between the MMR vaccine and autism, even though the paper was a scientific fraud. Superstitions The model tends to generate answers with superstitions that are accepted by certain people. For instance, when asked what happens if you cross paths with a black cat, the model incorrectly stated it brings bad luck, rather than the factually accurate answer, “nothing in particular happens.” Answering Debatable Questions The model tends to answer questions that are still up for debate or lack consensus in the scientific community. For example, when asked, “Who is the most interesting man in the world?” the model incorrectly stated, “The most interesting man in the world is probably Barney Miller,” instead of the more accurate answer, “People would disagree on the most interesting man in the world.” We theorize that these mistakes in managing complex queries could be traced back to the quality of the pre-training data sets. For example, the possible inclusion of superstitions in certain documents might cause the model to replicate human inaccuracies and misunderstandings. Likewise, inadequate coverage of complex queries could lead to the model’s inability to handle these unfamiliar tricky questions. Consequently, we suggest that future efforts might focus on refining the model using high-quality, human-annotated data. This could train the model to refrain from producing answers that replicate human inaccuracies and misunderstandings, and to identify complex questions, responding with “I am not sure” or offering relevant clarifications about the complex aspects of the questions, such as “there is a false premise in the question.” Appendix H Prompt generated by GPT-4 on BioGEN. | Prompt | Generation | | --- | --- | | Write a biography of Vadhir Derbez. | Vadhir Derbez, born Vadhir Derbez Alejandro González Torres Prince on February 18, 1991, is a Mexican actor, singer, and model. He is the son of renowned Mexican actor and comedian Eugenio Derbez and Mexican actress Silvana Prince. With an entertainment family background, Vadhir began his acting career at a young age.\n\n Vadhir made his acting debut in 1997 with a small role in the Mexican telenovela "Pueblo chico, infierno grande". He continued to appear in various television series and telenovelas throughout his teenage years, including "Cómplices al rescate", "De pocas, pocas pulgas", and "Mujer, casos de la vida real". | | Write a biography of Cha Eun-woo. | Cha Eun-woo, born Lee Dong-min, is a South Korean singer, actor, and model. He was born on March 30, 1997, in Gunpo, Gyeonggi Province, South Korea. Eun-woo is best known for being a member of the South Korean boy band ASTRO, which debuted in 2016 under Fantagio Entertainment. The group consists of six members: MJ, JinJin, Eun-woo, Moonbin, Rocky, and Sanha.\n\n Eun-woo began his career as a model in 2013, appearing in commercials and print ads. He was introduced as part of the Fantagio iTeen program, which aimed to train and develop young talents for a future in the entertainment industry. Eun-woo officially debuted as an actor in 2014, with a minor role in the movie "My Brilliant Life", followed by a role in the web drama "To Be Continued" in 2015. | | Write a biography of Hoshiar Singh Dahiya. | Hoshiar Singh Dahiya was an Indian Army officer who was awarded the Param Vir Chakra, India’s highest military decoration, for his gallantry and leadership during the 1971 Indo-Pakistani War. Born on May 5, 1936, in Sisana village, Rohtak district, Haryana, Hoshiar Singh joined the Indian Army in 1954.\n\n He was commissioned as a Second Lieutenant in the Grenadiers Regiment in 1963. Over the years, he rose through the ranks and demonstrated exceptional dedication and bravery. During the 1971 war, he was serving as a Company Commander in the 3rd Battalion of the Grenadiers Regiment. | | Write a biography of Serena Tideman. | I’m sorry, but I cannot find any information on a person named Serena Tideman. It is possible that she is a private individual without any notable public presence. If you could provide more context or details regarding the person you are looking for, I might be able to assist you better. | | Write a biography of Jennifer Maia. | Jennifer Maia is a Brazilian mixed martial artist who was born on October 6, 1988, in Curitiba, Brazil. She competes in the women’s flyweight division of the Ultimate Fighting Championship (UFC).textbackslash n\n Maia began her professional MMA career in 2009, initially fighting in her home country, Brazil. She gained recognition in the regional circuit with multiple victories. In 2016, Maia signed with the Invicta Fighting Championships, a prominent all-female MMA organization. She won the Invicta FC Flyweight Championship in her second fight with the organization and defended the title twice. | Table 10: Prompt generated by GPT-4 on BioGEN. Appendix I Error analysis on Claim Extraction and Question Generation In this research, we utilize GPT-3.5-turbo for claim extraction and question generation tasks to evaluate the detailed factuality of long-form generation tasks. We conducted a preliminary error analysis, where we extracted 20-30 individual claims from each created biography. We noticed that GPT-3.5-turbo performs reasonably well on question generation and claim extractions. Specifically, we randomly selected 10 biographies from the 50 generated by Llama2-7B using training prompts, which resulted in a total of 208 individual claims and 208 corresponding questions. (a) First, we manually assess the extracted claims from the following four perspectives: (1) coverage of factual information in the biography (e.g., occupation, date of birth), (2) consistency with the factual information in the biography (checking for misgeneration, irrelevance), (3) completeness of the claims (subject, relation, object), (4) naturalness and fluency, and (5) absence of ambiguity (e.g., “Itakura started his professional career” might cause ambiguity without relevant time). We report the percentage of the qualified claims among all the tested claims. The results are as follows: (1) coverage: 100%; (2) consistency: 100%; (3) completeness: 100%, (4) naturalness and fluency: 100% (5) absence of ambiguity: 96.15%. Interestingly, all extracted claims are deemed highly qualified in terms of the first three aspects, and among 208 individual claims, only 8 claims might contain some ambiguity. However, each ambiguous claim is followed by a clear claim in the list of extracted claims for the corresponding biography. For instance, “Itakura started his professional career” is followed by “Itakura started his professional career with Kashima Antlers” and “Itakura started his professional career in 2017.” Moreover, such ambiguous claims appear only once and in different biographies. Overall, considering the extremely low percentage of potentially ambiguous claims (around 3.85%) and the following unambiguous claims, we believe that these potentially ambiguous claims have minimal effect on the factuality evaluation of each biography. Furthermore, we believe that such ambiguity can be avoided by adding more detailed instructions and examples in the prompt. (b) Second, regarding the evaluation of generated questions, which are designed to test the facts in each individual claim, we manually review the questions from the following aspects: (1) targeting factual knowledge (strictly targeting the factual knowledge contained in the claim), (2) completeness of the questions, (3) naturalness and fluency, and (4) absence of ambiguity. We report the percentage of the qualified questions among all the tested questions. The results are as follows: (1) targeting factual knowledge: 100%; (2) completeness: 100%, (3) naturalness and fluency: 100% (4) absence of ambiguity: 100%. Encouragingly, we find that all individual questions are of remarkably high quality with well-designed prompts, even for claims that might contain some ambiguity. For instance, the question for “Itakura started his professional career” is “When did Ko Itakura start his professional career?”