SyntaxShap：面向文本生成的语法感知可解释性方法
SyntaxShap: Syntax-aware Explainability Method for Text Generation
AI总结：
本文提出 SyntaxShap，一种结合句法依赖树与 Shapley 值的文本生成局部可解释性方法，实验证明其能为自回归模型生成更忠实连贯的解释，并强调了可解释 AI 评估策略的必要性。
摘要：
为了在安全关键领域利用大型语言模型的能力，我们需要确保其预测的可解释性。然而，尽管模型可解释性受到了广泛关注，但使用针对文本数据量身定制的方法来解释序列到序列任务仍是一个未被探索的领域。本文提出了 SyntaxShap，这是一种用于文本生成的局部、模型无关的可解释性方法，它考虑了文本数据中的语法。本研究扩展了 Shapley 值以纳入基于解析的句法依赖关系。采用博弈论方法，SyntaxShap 仅考虑受依赖树约束的联盟。我们采用基于模型的评估，使用忠实度、连贯性以及解释与模型的语义对齐等多种指标，将 SyntaxShap 及其加权形式与适应于文本生成任务的最先进可解释性方法进行比较。我们表明，这种语法感知方法有助于为自回归模型的预测构建更忠实、更连贯的解释。面对人类与 AI 模型推理之间的不一致，本文还强调了可解释 AI 中谨慎评估策略的必要性。
英文摘要：
To harness the power of large language models in safety-critical domains, we need to ensure the explainability of their predictions. However, despite the significant attention to model interpretability, there remains an unexplored domain in explaining sequence-to-sequence tasks using methods tailored for textual data. This paper introduces SyntaxShap, a local, model-agnostic explainability method for text generation that takes into consideration the syntax in the text data. The presented work extends Shapley values to account for parsing-based syntactic dependencies. Taking a game theoric approach, SyntaxShap only considers coalitions constraint by the dependency tree. We adopt a model-based evaluation to compare SyntaxShap and its weighted form to state-of-the-art explainability methods adapted to text generation tasks, using diverse metrics including faithfulness, coherency, and semantic alignment of the explanations to the model. We show that our syntax-aware method produces explanations that help build more faithful and coherent explanations for predictions by autoregressive models. Confronted with the misalignment of human and AI model reasoning, this paper also highlights the need for cautious evaluation strategies in explainable AI.
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。
