检验LLMs对参数化知识之外问题的不确定性表达
Examining LLMs' Uncertainty Expression Towards Questions Outside Parametric Knowledge
AI总结：
本研究通过构建UnknownBench基准，系统评估LLMs在参数化知识外问题上的诚实性与有用性权衡，发现多数模型难以一致地表达不确定性或拒绝回答。
摘要：
大型语言模型（LLMs）在缺乏足够参数化知识以生成合理回答的情况下，能否表达其不确定性？本研究旨在系统调查LLMs在此类情境中的行为，强调诚实性与有用性之间的权衡。为应对精确界定LLMs知识空白的挑战，我们诊断性地构造了包含不存在概念或错误前提的无法回答的问题，确保它们处于LLMs海量训练数据之外。通过编制一个包含无法回答和可回答问题的基准UnknownBench，我们定量评估LLMs在保持有用性的同时维持诚实性的表现。使用一种与模型无关的统一置信度引出方法，我们观察到大多数LLMs未能一致地拒绝或对参数化知识之外的问题表达不确定性，尽管指令微调和对齐技术能提供边际改进。此外，LLMs的不确定性表达并不总是与其文本输出所感知的置信度保持一致。
英文摘要：
Can large language models (LLMs) express their uncertainty in situations where they lack sufficient parametric knowledge to generate reasonable responses? This work aims to systematically investigate LLMs' behaviors in such situations, emphasizing the trade-off between honesty and helpfulness. To tackle the challenge of precisely determining LLMs' knowledge gaps, we diagnostically create unanswerable questions containing non-existent concepts or false premises, ensuring that they are outside the LLMs' vast training data. By compiling a benchmark, UnknownBench, which consists of both unanswerable and answerable questions, we quantitatively evaluate the LLMs' performance in maintaining honesty while being helpful. Using a model-agnostic unified confidence elicitation approach, we observe that most LLMs fail to consistently refuse or express uncertainty towards questions outside their parametric knowledge, although instruction fine-tuning and alignment techniques can provide marginal enhancements. Moreover, LLMs' uncertainty expression does not always stay consistent with the perceived confidence of their textual outputs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
