Anthropic Paper: Evaluating LLM Behavior Explanations via Counterfactual Prompts
dl_weekly · x · 2026-08-29
Anthropic researchers introduce CHIVE, an agentic pipeline to discover and explain in-the-wild LLM behaviors.
- Method: CHIVE tests and explains unexpected LLM behaviors via counterfactual prompt edits, validating explanations by checking if input modifications alter outputs.
- Finding: Experiments show that agents equipped with interpretability tools like activation reading do not predict experiment outcomes better than agents that only read the transcript. This suggests current internal state explanation tools may lack additional predictive value.
- Application: The resulting dataset can also train models to predict how prompt edits change their behavior, showing generalization to held-out settings.
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