ICE: Intervention-Consistent Explanation Evaluation with Statistical Grounding for LLMs
Evaluating whether explanations faithfully reflect a model's reasoning remains an open problem. Existing benchmarks use single interventions without statistical testing, making it impossible to distinguish genuine faithfulness from chance-level performance. We show that faithfulness is not a fixed property but an operator-dependent quantity that changes with the intervention method used to measure it. We introduce ICE (Intervention-Consistent Explanation), a framework that evaluates explanations against random baselines of equal size under multiple operators. Evaluating 7 LLMs across 4 tasks with deletion and retrieval infill operators, we find that switching operators crosses the positiv...
| Authors | Abhinaba Basu, Pavan Chakraborty |
|---|---|
| Primary category | cs.CL |
| Published | 19 March 2026 |
| Last updated at source | 2 September 2026 |
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