通过更多思考减少幻觉：基于方面的因果回避用于大语言模型
Hallucinate Less by Thinking More: Aspect-Based Causal Abstention for Large Language Models
AI总结：
本文提出基于方面的因果回避方法，通过分析LLM知识的内部多样性，实现早期回避以减少幻觉。
摘要：
大语言模型（LLMs）常常生成流畅但事实错误的响应，这种现象称为幻觉。回避，即模型选择不回答而输出如“我不知道”之类的短语，是一种常见的安全措施。然而，现有的回避方法通常依赖于生成后的信号，如生成变化或反馈，这限制了它们在事前防止不可靠响应的能力。在本文中，我们引入了基于方面的因果回避（ABCA），一种新的框架，通过因果推理分析LLM知识的内部多样性，实现早期回避。这种多样性反映了从各种来源获取的参数化知识的多面性，代表了学科、法律语境或时间框架等多样的方面。ABCA通过这些方面估计因果效应，以评估与给定查询相关知识的可靠性。基于这些估计，我们启用了两种类型的回避：类型1，其中方面效应不一致（知识冲突），以及类型2，其中方面效应一致支持回避（知识不足）。在标准基准上的实验表明，ABCA提高了回避的可靠性，实现了最先进的性能，并增强了回避决策的可解释性。
英文摘要：
Large Language Models (LLMs) often produce fluent but factually incorrect responses, a phenomenon known as hallucination. Abstention, where the model chooses not to answer and instead outputs phrases such as "I don't know", is a common safeguard. However, existing abstention methods typically rely on post-generation signals, such as generation variations or feedback, which limits their ability to prevent unreliable responses in advance. In this paper, we introduce Aspect-Based Causal Abstention (ABCA), a new framework that enables early abstention by analysing the internal diversity of LLM knowledge through causal inference. This diversity reflects the multifaceted nature of parametric knowledge acquired from various sources, representing diverse aspects such as disciplines, legal contexts, or temporal frames. ABCA estimates causal effects conditioned on these aspects to assess the reliability of knowledge relevant to a given query. Based on these estimates, we enable two types of abstention: Type-1, where aspect effects are inconsistent (knowledge conflict), and Type-2, where aspect effects consistently support abstention (knowledge insufficiency). Experiments on standard benchmarks demonstrate that ABCA improves abstention reliability, achieves state-of-the-art performance, and enhances the interpretability of abstention decisions.
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