Uncertainty Quantification for Large Language Models as Multi-step Decision Makers
Authors: Runing Yang, Jianfeng He, Shangding Gu, Ming Jin


Reviewer paper summaries: The paper addresses the challenge of estimating the trustworthiness of responses generated by Large Language Models (LLMs), particularly in the context of multi-step decision-making and natural language generation tasks. The authors propose a framework that treats the response generation process as a Markov Decision Process (MDP) and uses the concept of entropy aggregation across intermediate steps to estimate prediction uncertainty.
Summary of strengths: 1. The paper introduces a novel framework for uncertainty quantification in Large Language Models (LLMs) by formulating the response generation process as a Markov Decision Process (MDP). 
2. The paper is well-organized, with a clear presentation of the problem, related work, methodology, experiments, and conclusions.
Summary of weaknesses: 1. The authors assume a correlation between high uncertainty and incorrect responses, and low uncertainty with correct responses. However, this assumption needs further proof.
2. As the intermediate-state sentences accumulate over steps, their lengths grow, which could present challenges in accurately clustering semantically similar states. This might affect the precision of the uncertainty estimation, especially for tasks that involve longer responses.
3. The experiments are conducted on specific NLP question-answer datasets with inspectable reference answers. It is unclear how the findings would translate to more complex or varied datasets, which could limit the perceived impact of the methods.
Comments, suggestions and typos: See "Summary Of Weaknesses"
Overall assessment scores: 3.5