To Reason or Not to: Selective Chain-of-Thought in Medical Question Answering
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866911463176667136 |
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| author | Zhan, Zaifu Zeng, Min Zhou, Shuang Song, Yiran Chen, Xiaoyi Hou, Yu Wu, Yifan Ruan, Yang Zhang, Rui |
| author_facet | Zhan, Zaifu Zeng, Min Zhou, Shuang Song, Yiran Chen, Xiaoyi Hou, Yu Wu, Yifan Ruan, Yang Zhang, Rui |
| contents | Objective: To improve the efficiency of medical question answering (MedQA) with large language models (LLMs) by avoiding unnecessary reasoning while maintaining accuracy.
Methods: We propose Selective Chain-of-Thought (Selective CoT), an inference-time strategy that first predicts whether a question requires reasoning and generates a rationale only when needed. Two open-source LLMs (Llama-3.1-8B and Qwen-2.5-7B) were evaluated on four biomedical QA benchmarks-HeadQA, MedQA-USMLE, MedMCQA, and PubMedQA. Metrics included accuracy, total generated tokens, and inference time.
Results: Selective CoT reduced inference time by 13-45% and token usage by 8-47% with minimal accuracy loss ($\leq$4\%). In some model-task pairs, it achieved both higher accuracy and greater efficiency than standard CoT. Compared with fixed-length CoT, Selective CoT reached similar or superior accuracy at substantially lower computational cost.
Discussion: Selective CoT dynamically balances reasoning depth and efficiency by invoking explicit reasoning only when beneficial, reducing redundancy on recall-type questions while preserving interpretability.
Conclusion: Selective CoT provides a simple, model-agnostic, and cost-effective approach for medical QA, aligning reasoning effort with question complexity to enhance real-world deployability of LLM-based clinical systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_20130 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | To Reason or Not to: Selective Chain-of-Thought in Medical Question Answering Zhan, Zaifu Zeng, Min Zhou, Shuang Song, Yiran Chen, Xiaoyi Hou, Yu Wu, Yifan Ruan, Yang Zhang, Rui Computation and Language Artificial Intelligence Objective: To improve the efficiency of medical question answering (MedQA) with large language models (LLMs) by avoiding unnecessary reasoning while maintaining accuracy. Methods: We propose Selective Chain-of-Thought (Selective CoT), an inference-time strategy that first predicts whether a question requires reasoning and generates a rationale only when needed. Two open-source LLMs (Llama-3.1-8B and Qwen-2.5-7B) were evaluated on four biomedical QA benchmarks-HeadQA, MedQA-USMLE, MedMCQA, and PubMedQA. Metrics included accuracy, total generated tokens, and inference time. Results: Selective CoT reduced inference time by 13-45% and token usage by 8-47% with minimal accuracy loss ($\leq$4\%). In some model-task pairs, it achieved both higher accuracy and greater efficiency than standard CoT. Compared with fixed-length CoT, Selective CoT reached similar or superior accuracy at substantially lower computational cost. Discussion: Selective CoT dynamically balances reasoning depth and efficiency by invoking explicit reasoning only when beneficial, reducing redundancy on recall-type questions while preserving interpretability. Conclusion: Selective CoT provides a simple, model-agnostic, and cost-effective approach for medical QA, aligning reasoning effort with question complexity to enhance real-world deployability of LLM-based clinical systems. |
| title | To Reason or Not to: Selective Chain-of-Thought in Medical Question Answering |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2602.20130 |