Faithfulness Serum：通过归因引导缓解文本解释中LLM决策的忠实性差距
Faithfulness Serum: Mitigating the Faithfulness Gap in Textual Explanations of LLM Decisions via Attribution Guidance
AI总结：
本文通过归因引导方法提升LLM决策解释的忠实性，验证了现有解释的epistemic忠实性不足，并展示了改进方法在多个模型和基准上的有效性。
摘要：
大型语言模型（LLMs）在NLP领域表现出色，但其缺乏可解释性使其被视为黑箱，限制了在需要透明和信任的领域中的应用。一种有前景的解决方向是事后基于文本的解释，旨在用自然语言解释模型决策。先前工作集中在生成看似主观忠实的合理解释，但不清楚这些解释是否在epistemically忠实，即是否反映了模型实际依赖的内部证据。在本文中，我们首先通过反事实评估了LLM生成解释的epistemic忠实性，证明它们通常不忠实。我们随后引入一种无需训练的方法，通过注意力层面的干预来增强忠实性，该方法受通过忠实归因方法提取的token级热图指导。该方法在多个模型、基准和提示上显著提高了epistemic忠实性。
英文摘要：
Large language models (LLMs) achieve strong performance and have revolutionized NLP, but their lack of explainability keeps them treated as black boxes, limiting their use in domains that demand transparency and trust. A promising direction to address this issue is post-hoc text-based explanations, which aim to explain model decisions in natural language. Prior work has focused on generating convincing rationales that appear to be subjectively faithful, but it remains unclear whether these explanations are epistemically faithful, whether they reflect the internal evidence the model actually relied on for its decision. In this paper, we first assess the epistemic faithfulness of LLM-generated explanations via counterfactuals and show that they are often unfaithful. We then introduce a training-free method that enhances faithfulness by guiding explanation generation through attention-level interventions, informed by token-level heatmaps extracted via a faithful attribution method. This method significantly improves epistemic faithfulness across multiple models, benchmarks, and prompts.
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。
