Can LLM s Learn Uncertainty on Their Own? Expressing Uncertainty Effectively in A Self-Training Manner (2024)

Abstract: Large language models (LLMs) often exhibit excessive, random, and uninformative uncertainty, rendering them unsuitable for decision-making in human-computer interactions. In this paper, we aim to instigate a heightened awareness of self-uncertainty in LLMs, enabling them to express uncertainty more effectively. To accomplish this, we propose an uncertainty-aware instruction tuning (UaIT) method, aligning LLMs’ perception with the probabilistic uncertainty of the generation. We conducted experiments using LLaMA2 and Mistral on multiple free-form QA tasks. Experimental results revealed a surprising 45.2% improvement in the effectiveness of uncertainty expression by LLMs, accompanied by reasonably good out-of-domain generalization capabilities. Moreover, this uncertainty expression can serve as a valuable real-time basis for human decision-making, e.g., retrieving external documents and incorporating stronger LLMs.

## introduction large language models (llms), such as chatgpt and gpt-4, are capable of generating fluent and realistic responses tailored to diverse user requirements @xcite . however, llms do not consistently exhibit optimal performance, as they can also generate unreliable responses characterized by hallucinations or factual errors. effective uncertainty estimation is widely recognized as a crucial step in establishing reliable ai systems, as it provides a foundation for decision-making in human-machine interactions. unlike previously examined models with distinct labels (e.g. classification), uncertainty estimation for free-form llm poses a significant challenge due to the inherent flexibility in generation and the * co-corresponding author 1 code and scripts can be found at: https://github.com/ nlp2ct/uait @xcite what is the capital of france? ## related work uncertainty estimation constitutes an essential step in developing reliable ai systems, which are instrumental in detecting unreliable responses characterized by hallucinations @xcite or factual errors @xcite generated by llms. traditional uncertainty estimation methods have mainly focused on text classification @xcite @xcite or regression @xcite @xcite tasks with clear and distinct labels. however, for free-form llms, multiple different but semantically equivalent generations can be considered correct. recent research transformed the free-form questions into multiple-choice form to align with traditional categorical uncertainty estimation methods @xcite @xcite . some recent works estimated the uncertainty by quantifying the consistency of multiple generations, computing predictive entropies with generations, or incorporating paraphrase detection @xcite @xcite @xcite . semantic entropy (se) @xcite proposes the notion of "semantic equivalence" to aggregate generations with similar semantics. sar @xcite advocates assigning more attention to tokens and sentences with higher relevance. the research on estimating uncertainty with the expression of llm is still in its early stages. recent research has explored various prompt strategies to enhance the uncertainty expression @xcite @xcite . @xcite group examples based on the mathematical computation type and fine-tune llms with the empirical accuracy of each group to predict the correctness of problem-solving. however, this group-based method, where the answer comprises solely a single numerical token, lacks generality in applications. @xcite further combine direct expression and multi-sampling methods to achieve more accurate assessment. @xcite analyze the correlation between internal model probability and the verbalized uncertainty expression. another concurrent work develops a comprehensive framework that incorporates sampling, clustering, and the use of external llms (gpt-4) to generate rationales, to enhance the uncertainty expression @xcite . our work focuses on enhancing uncertainty awareness in llms by simply aligning powerful probabilistic uncertainty estimation and utilizing output uncertainty as a basis for real-time human decision-making. we highlight that such a simplified approach that avoids extensive multi-sampling and reliance on external commercial llms (e.g. gpt4 or chatgpt), is capable of demonstrating robust and immediate uncertainty expression in real-time interactions. ## conclusion expressing uncertainty by llms poses a significant challenge that has not been thoroughly explored. in this paper, we address this challenge by training the model to align the probabilistic uncertainty of its own generation, thereby enhancing the model's ability to perceive and express uncertainty. experimental results demonstrate that the model not only exhibits strong uncertainty expression capabilities within the domain but also showcases promising generalization capabilities.