HiMed: Incentivizing Hindi Reasoning in Medical LLMs
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Computer Science > Computation and Language
Title:HiMed: Incentivizing Hindi Reasoning in Medical LLMs
Abstract:Medical large language models hold promise for reducing healthcare disparities, yet Hindi remains severely underrepresented. While medical LLMs excel in high-resource languages, their performance degrades sharply in Hindi, particularly on Indian systems of medicine. We argue that robust cross-lingual medical transfer requires Hindi reasoning. To this end, we introduce HiMed, a Hindi reasoning medical corpus and benchmark suite covering both Western and Indian medicine. We further propose HiMed-8B, a Hindi-form medical reasoning LLM, through the design of decaying scaffolding reward. Extensive experiments demonstrate improvement in Hindi medical reasoning performance and reduction in the English--Hindi accuracy gap. Ablation studies validate the contribution of each training stage and reward component. All data and code are available on GitHub: this https URL.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2605.24635 [cs.CL] |
| (or arXiv:2605.24635v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2605.24635
arXiv-issued DOI via DataCite (pending registration)
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