New benchmark reveals AI assistants often overassist users

A new benchmark called Int-Bench has been developed to evaluate how AI assistants intervene in user problem-solving processes. Researchers found that current large language models (LLMs) tend to intervene too frequently and provide complete solutions, which may hinder long-term learning and cognitive engagement. In contrast to human tutors, LLMs appear to prioritize short-term task success over fostering deeper reasoning skills. AI

IMPACT Current AI assistants may hinder long-term learning by over-intervening and providing direct solutions instead of guiding user reasoning.

RANK_REASON The cluster contains a research paper introducing a new benchmark for evaluating AI assistant behavior. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• Int-Bench
• Large language models
• Max Kleiman-Weiner

• paper
• safety

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