用语义聚类量化大模型不确定性，提升生成可靠性。
Addressing Uncertainty in LLMs to Enhance Reliability in Generative AI
- 基于餐厅过程动态聚类语义，计算生成结果的熵值衡量不确定性。
- 在COQA和TriviaQA上实现最优不确定性评估性能，指标全面领先。
- 生成更小预测集仍保证正确响应覆盖率，适合高可靠场景使用。
本文提出一种受中国餐馆过程启发的动态语义聚类方法，用于量化大语言模型（LLM）在推理中的不确定性。通过计算生成语义簇的熵来衡量给定查询下的不确定性，并将这些簇的（负）似然作为置信预测框架中的（非）符合度评分，使模型可输出一组响应而非单一输出，从而显式处理预测不确定性。我们在两个知名问答基准数据集COQA和TriviaQA上验证了该方法的有效性，采用Llama2和Mistral两种模型。实验表明，所提不确定性量化技术在AUROC、AUARC和AURAC等指标上达到当前最优（SOTA）表现。此外，该置信预测器在保持相同正确响应覆盖概率的前提下，生成的预测集合比现有SOTA基线更小。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。