LLMs exhibit "Thinking Inertia", continuing to infer when told not to

A new research paper introduces the concept of "Thinking Inertia" in large language models (LLMs), observing that these models continue to exhibit explicit inference even when instructed not to. The study proposes new metrics to evaluate "no-thinking" behavior, distinguishing between answer-only compliance, relevant but non-inferential text, and explicit inference. Findings indicate that current LLM controls are insufficient to reliably eliminate inference, particularly in open-ended tasks, suggesting a trade-off between strict answer-only compliance and task accuracy. AI

IMPACT Highlights a fundamental limitation in current LLM control mechanisms, suggesting a need for new evaluation methods beyond simple instruction following.

RANK_REASON Academic paper detailing a new phenomenon and proposed metrics for LLM behavior. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

• Boolean
• Empty-Thinking Rate
• Hugging Face
• large-language models
• LLM-as-judge Explicit Inference Rate
• multiple choice questionnaire
• Open-ended questions in surveys of patients' satisfaction with family doctors
• Question-Pre-answer Relevance
• Thinking Inertia

• paper
• safety

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