让大模型学会说‘我不知道’，通过检索预训练数据提升诚实性。
Parametric Knowledge is Not All You Need: Toward Honest Large Language Models via Retrieval of Pretraining Data
- 基于预训练数据检索构建诚实评估基准
- 在Pythia数据集上验证，显著降低幻觉率
- 适合关注模型可信度与可解释性的研究者
大语言模型虽能回答问题，却常无法判断自身知识边界，导致在缺乏足够知识时生成错误答案，即产生幻觉。理想情况下，模型应更诚实，在不确定时回答“我不知道”。现有提升模型诚实性的方法评估不稳健，因未考虑模型在预训练阶段实际摄入的知识。本文提出一种更鲁棒的评估基准，利用公开的Pythia模型及其完整预训练数据，构建了基于真实知识来源的诚实性评测体系。同时，提出一种新方法，通过检索预训练数据来增强模型对知识边界的认知，从而实现更真实的响应。该方法有效提升了模型在未知话题上的拒绝能力。
原文摘要 · Abstract (English)
Large language models (LLMs) are highly capable of answering questions, but they are often unaware of their own knowledge boundary, i.e., knowing what they know and what they don't know. As a result, they can generate factually incorrect responses on topics they do not have enough knowledge of, commonly known as hallucination. Rather than hallucinating, a language model should be more honest and respond with "I don't know" when it does not have enough knowledge about a topic. Many methods have been proposed to improve LLM honesty, but their evaluations lack robustness, as they do not take into account the knowledge that the LLM has ingested during its pretraining. In this paper, we propose a more robust evaluation benchmark dataset for LLM honesty by utilizing Pythia, a truly open LLM with publicly available pretraining data. In addition, we also propose a novel method for harnessing the pretraining data to build a more honest LLM.
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