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.
Both Llama3 and Mistral have a low error rate with 2% and 5% respectively
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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.