NeuroFaith：通过内部表示对齐评估LLM自解释的忠实性
NeuroFaith: Evaluating LLM Self-Explanation Faithfulness via Internal Representation Alignment
AI总结：
NeuroFaith通过内部表示对齐评估LLM自解释的忠实性，提出了一种灵活的框架和线性探针以提高模型的解释可信度。
摘要：
大型语言模型（LLMs）可以生成合理且自由的文本自解释来证明其答案。然而，这些自然语言解释可能无法准确反映模型的实际推理过程，指出存在缺乏忠实性的现象。现有的忠实性评估方法主要依赖于行为测试或计算块分析，而没有检查内部神经表示的语义内容。本文提出了NeuroFaith，一个灵活的框架，通过在解释中识别关键概念，并机械地测试这些概念是否确实影响模型的预测，来衡量LLM自由文本自解释的忠实性。我们展示了NeuroFaith在双跳推理和分类任务中的通用性。此外，我们开发了一种基于NeuroFaith的线性忠实性探针，用于从表示空间中检测不忠实的自解释，并通过引导提高忠实性。NeuroFaith提供了一种评估和增强LLM自由文本自解释忠实性的系统方法，满足了对可信AI系统的关键需求。
英文摘要：
Large Language Models (LLMs) can generate plausible free text self-explanations to justify their answers. However, these natural language explanations may not accurately reflect the model's actual reasoning process, pinpointing a lack of faithfulness. Existing faithfulness evaluation methods rely primarily on behavioral tests or computational block analysis without examining the semantic content of internal neural representations. This paper proposes NeuroFaith, a flexible framework that measures the faithfulness of LLM free text self-explanation by identifying key concepts within explanations and mechanistically testing whether these concepts actually influence the model's predictions. We show the versatility of NeuroFaith across 2-hop reasoning and classification tasks. Additionally, we develop a linear faithfulness probe based on NeuroFaith to detect unfaithful self-explanations from representation space and improve faithfulness through steering. NeuroFaith provides a principled approach to evaluating and enhancing the faithfulness of LLM free text self-explanations, addressing critical needs for trustworthy AI systems.
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。
