用因果推理提升多语言大模型的可信拒答能力
CausalAbstain: Enhancing Multilingual LLMs with Causal Reasoning for Trustworthy Abstention
- 从因果视角设计反馈选择机制，判断何时采纳多语言反馈
- 在双语数据集上显著提升拒答准确率，减少幻觉生成
- 适合需要高可信度拒答的多语言应用，如跨语言问答系统
大型语言模型在不同语言间存在知识差距。在知识不足时让模型主动拒答，是降低多语言场景下幻觉的有效策略。现有方法依赖模型生成多语言反馈并自我反思，但易受生成反馈中错误和偏见影响。为此，我们提出基于因果视角的CausalAbstain方法，帮助模型判断是否采纳多个生成反馈，并识别最有用的反馈。大量实验表明，该方法在原生语言（Casual-native）和多语言（Causal-multi）设置下均有效提升拒答决策性能与可解释性，在涵盖百科与常识知识的两个基准数据集上优于强基线。代码与数据已开源。
原文摘要 · Abstract (English)
Large Language Models (LLMs) often exhibit knowledge disparities across languages. Encouraging LLMs to \textit{abstain} when faced with knowledge gaps is a promising strategy to reduce hallucinations in multilingual settings. Current abstention strategies for multilingual scenarios primarily rely on generating feedback in various languages using LLMs and performing self-reflection. However, these methods can be adversely impacted by inaccuracies and biases in the generated feedback. To address this, from a causal perspective, we introduce \textit{CausalAbstain}, a method that helps LLMs determine whether to utilize multiple generated feedback responses and how to identify the most useful ones. Extensive experiments demonstrate that \textit{CausalAbstain} effectively selects helpful feedback and enhances abstention decisions with interpretability in both native language (\textsc{Casual-native}) and multilingual (\textsc{Causal-multi}) settings, outperforming strong baselines on two benchmark datasets covering encyclopedic and commonsense knowledge QA tasks. Our code and data are open-sourced at https://github.com/peachch/CausalAbstain.
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