Self-explanations from LLMs produce faithful token subsets for correct predictions but align with human rationales only conditionally on text length and task complexity, unlike post-hoc attribution methods that highlight structural tokens.
In Proceedings of the 2021 Conference on Empiri- cal Methods in Natural Language Processing, pages 1597–1611, Online and Punta Cana, Dominican Re- public
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A Systematic Comparison between Extractive Self-Explanations and Human Rationales in Text Classification
Self-explanations from LLMs produce faithful token subsets for correct predictions but align with human rationales only conditionally on text length and task complexity, unlike post-hoc attribution methods that highlight structural tokens.