LLM Self-Explanations

Self-explanation capability: LLMs can self-generate feature-attribution explanations that meaningfully highlight the tokens driving their predictions.

Performance + truthfulness: Self-explanation improves both task performance and the truthfulness of outputs compared to baseline prompting.

CoT synergy: Combines productively with chain-of-thought prompting, giving additive improvements rather than substituting for it.

Interpretability lever: Offers a cheap, model-agnostic interpretability pattern that works through the API without needing gradients or white-box access.