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How Do LLMs Really Reason? A Framework to Separate Logic from Knowledge
- Advancements in reasoning-focused LLMs like OpenAI's o1/3 and DeepSeek-R1 have improved complex task performance, yet their reasoning processes remain unclear.
- Current evaluations of LLMs often focus on final-answer accuracy, masking the reasoning steps and the combination of knowledge and logic.
- Factual errors and lack of reasoning depth in math and medicine demonstrate the limitations of current final-answer evaluation methods.
- Researchers propose a new framework to assess LLM reasoning by separating factual knowledge and logical steps using the Knowledge Index and Information Gain metrics.
- Evaluation of Qwen models across math and medicine tasks shows that reasoning skills do not easily transfer between domains.
- The study compares supervised fine-tuning and reinforcement learning in domain-specific tasks, highlighting the impact on accuracy, knowledge retention, and reasoning depth.
- Results indicate that while supervised fine-tuning enhances factual accuracy, it may weaken reasoning depth, whereas reinforcement learning improves both reasoning and knowledge.
- The framework introduced in the study aims to make LLMs more interpretable and trustworthy, particularly in critical fields like medicine and math.
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