Researchers have introduced CarryOnBench, a new benchmark designed to evaluate how well large language models can recover helpfulness in multi-turn conversations after a user clarifies their intent. The benchmark simulates over 5,900 conversations across 14 models, revealing that many models initially withhold information due to misinterpretation rather than lack of knowledge. While most models improve with clarification, some exhibit failure modes like utility lock-in or unsafe recovery, which are missed by single-turn evaluations. AI
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IMPACT Highlights a critical gap in LLM safety evaluations, suggesting current methods may overlook models that are unresponsive to user clarification.
RANK_REASON Academic paper introducing a new benchmark for LLM safety and utility.