Evaluating Explanations of LLM Behavior In The Wild with Counterfactual Experiments

per LessWrong, researchers have introduced CHIVE, an agentic pipeline that uncovers hidden LLM behaviors by testing counterfactual prompt edits. In trials, these explanations failed to boost interpretability tools, as agents using activation oracles or natural‑language autoencoders performed no better than those reading transcripts alone. The findings suggest that current interpretability methods may not reliably predict model responses.

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