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cs.CL2025★ 1 cited
Disentangling Reasoning and Knowledge in Medical Large Language Models
Rahul Thapa, Qingyang Wu, Kevin Wu +11
Medical reasoning in large language models (LLMs) aims to emulate clinicians' diagnostic thinking, but current benchmarks such as MedQA-USMLE, MedMCQA, and PubMedQA often mix reaso…
cs.CL2025
Generative Evaluation of Complex Reasoning in Large Language Models
Haowei Lin, Xiangyu Wang, Ruilin Yan +7
With powerful large language models (LLMs) demonstrating superhuman reasoning capabilities, a critical question arises: Do LLMs genuinely reason, or do they merely recall answers f…
cs.CL2025
Efficient and Asymptotically Unbiased Constrained Decoding for Large Language Models
Haotian Ye, Himanshu Jain, Chong You +4
In real-world applications of large language models, outputs are often required to be confined: selecting items from predefined product or document sets, generating phrases that co…