This paper introduces SaySelf, a novel framework designed to address the limitations of Large Language Models (LLMs) in expressing confidence and acknowledging uncertainty. The key contributions and findings are:
Problem: LLMs often generate inaccurate information (hallucination) and struggle to convey their confidence levels, limiting their reliability and trustworthiness.
Proposed Solution: SaySelf
SaySelf is a two-stage training framework that aims to equip LLMs with the ability to express fine-grained confidence estimates and generate self-reflective rationales explaining their uncertainty.
Stage 1: Supervised Fine-Tuning
Multiple Sampling and Clustering: For each question, SaySelf samples multiple responses from a vanilla LLM and clusters them based on semantic similarity using an instruction-finetuned text embedding model (Instructor).
Confidence Estimation: The confidence score for a response is calculated based on the size of its cluster, reflecting the consistency among different reasoning paths.
Rationale Generation: GPT-4 analyzes the inconsistencies in the selected responses from different clusters and summarizes the uncertainties in natural language from a first-person perspective, generating the self-reflective rationale.
Dataset Creation & Fine-tuning: This process creates a dataset comprising questions, answers, confidence estimates, and self-reflective rationales. The vanilla LLM is then fine-tuned on this dataset.
Stage 2: Reinforcement Learning from Task Supervision
Reward Function: A reward function is designed to encourage accurate, high-confidence predictions and penalize overconfidence in incorrect answers.
Calibration with PPO: Proximal Policy Optimization (PPO) algorithm is employed to further calibrate the LLM’s confidence estimates based on the reward function.
Evaluation:
The paper evaluates SaySelf on various knowledge-intensive question-answering datasets, including HotpotQA, TruthfulQA, StrategyQA, FEVER, HaluEval, and ParaRel.
Key Findings:
Improved Calibration: SaySelf significantly reduces the confidence calibration error (ECE) and achieves higher AUROC scores compared to baseline methods, indicating a stronger correlation between expressed confidence and actual performance.
Maintained Task Performance: SaySelf maintains comparable or even slightly improved task accuracy compared to baselines, demonstrating that confidence elicitation does not compromise the LLM’s ability to answer questions correctly.
Faithful Rationales: The generated self-reflective rationales are found to be faithful and effectively capture the LLM’s internal uncertainties.
Strengths:
Novel Approach: SaySelf introduces a novel combination of supervised fine-tuning and reinforcement learning to address both confidence elicitation and rationale generation.
Fine-grained Confidence: Unlike previous methods that often produce binary or coarse-grained confidence estimates, SaySelf enables LLMs to express more nuanced confidence levels.
Self-Reflection: The generation of self-reflective rationales provides valuable insights into the LLM’s reasoning process and the sources of its uncertainty.
Limitations:
Dependence on GPT-4: The rationale generation process relies on GPT-4, which might limit the scalability and accessibility of the framework.
Computational Cost: The multi-step sampling and clustering process, along with the reinforcement learning stage, can be computationally expensive.
Impact and Future Directions:
SaySelf has the potential to significantly enhance the trustworthiness and reliability of LLMs by enabling them to express confidence and provide explanations for their uncertainty. This can lead to:
Improved Human-AI Collaboration: More reliable confidence estimates can facilitate better human-AI collaboration by allowing users to appropriately interpret and rely on LLM-generated outputs.
Targeted Knowledge Acquisition: Self-reflective rationales can highlight areas where the LLM lacks knowledge, guiding future training efforts and enabling more efficient knowledge acquisition.
Enhanced Explainability: The ability to generate self-reflective rationales contributes to the explainability of LLMs, fostering trust and transparency in their decision-making process.
Overall, SaySelf represents a significant step towards developing more reliable, transparent, and trustworthy LLMs. Future research can explore alternative methods for rationale generation, reduce computational costs, and investigate the application of SaySelf in different domains and tasks.
This paper introduces SaySelf, a novel framework designed to address the limitations of Large Language Models (LLMs) in expressing confidence and acknowledging uncertainty. The key contributions and findings are:
Problem: LLMs often generate inaccurate information (hallucination) and struggle to convey their confidence levels, limiting their reliability and trustworthiness.
Proposed Solution: SaySelf
SaySelf is a two-stage training framework that aims to equip LLMs with the ability to express fine-grained confidence estimates and generate self-reflective rationales explaining their uncertainty.
Stage 1: Supervised Fine-Tuning
Stage 2: Reinforcement Learning from Task Supervision
Evaluation:
The paper evaluates SaySelf on various knowledge-intensive question-answering datasets, including HotpotQA, TruthfulQA, StrategyQA, FEVER, HaluEval, and ParaRel.
Key Findings:
Strengths:
Limitations:
Impact and Future Directions:
SaySelf has the potential to significantly enhance the trustworthiness and reliability of LLMs by enabling them to express confidence and provide explanations for their uncertainty. This can lead to:
Overall, SaySelf represents a significant step towards developing more reliable, transparent, and trustworthy LLMs. Future research can explore alternative methods for rationale generation, reduce computational costs, and investigate the application of SaySelf in different domains and tasks.