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Automated benchmarks for explainability are fundamentally brittle: LLM "simulators" consistently game the metric by solving the task directly through semantic priors or exploiting label leakage rather than actually relying on the explanations.
LLMs can power interactive explanations in AI planning, letting users explore the plan space through natural language and improving trust compared to template-based approaches.
