In this paper, we present a dynamic semantic clustering approach inspired by the Chinese Restaurant Process, aimed at addressing uncertainty in the inference of Large Language Models (LLMs). We quantify uncertainty of an LLM on a given query by calculating entropy of the generated semantic clusters. Further, we propose leveraging the (negative) likelihood of these clusters as the (non)conformity score within Conformal Prediction framework, allowing the model to predict a set of responses instead of a single output, thereby accounting for uncertainty in its predictions. We demonstrate the effectiveness of our uncertainty quantification (UQ) technique on two well known question answering benchmarks, COQA and TriviaQA, utilizing two LLMs, Llama2 and Mistral. Our approach achieves SOTA performance in UQ, as assessed by metrics such as AUROC, AUARC, and AURAC. The proposed conformal predictor is also shown to produce smaller prediction sets while maintaining the same probabilistic guarantee of including the correct response, in comparison to existing SOTA conformal prediction baseline.

本文提出了一种动态语义聚类方法，该方法受到中国餐厅过程的启发，旨在解决大语言模型（LLMs）推理中的不确定性问题。研究表明，该方法通过量化语义聚类的熵来评估不确定性，并在符合预测框架内利用聚类的负似然度作为不符合性评分，从而实现多个响应的预测，显著提升了生成响应的可靠性。

解决大语言模型中的不确定性以增强生成性人工智能的可靠性