  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.
