arXiv：cs.CL· Umaira Izhar, Gunjan Arora, Pushpendra Singh·· 3 小时前
MH-INDIC：面向印度孕产妇健康 LLM 交互的文化对齐评测框架
Measuring Cultural Alignment Beyond the Average: A Framework for Evaluating Maternal-Health LLM Interactions in Indian Contexts
AI 导读
研究团队提出 MH-INDIC，一个面向北印度城市及半城市化语境孕产妇健康交互的文化对齐评测框架，用 10 个孕产妇健康推理维度刻画文化行为，并基于 102 位孕产妇的 26 项问卷评测了 10 个 LLM。
正文
Abstract:Existing evaluation methods for healthcare LLMs primarily assess factual correctness,safety, and fluency, while providing limited insight into whether generated interactions reflect culturally situated healthcare reasoning. This limitation is particularly important in maternal health, where care decisions are shaped by social and relational norms. We introduce MH-INDIC, a culturally grounded evaluation framework for maternal-health interactions in urban and semi-urban North Indian contexts that operationalises cultural behaviour through ten dimensions of maternal-health reasoning. Using a 26-item survey administered to 102 pregnant and postpartum women from urban and semi-urban North India, we evaluate ten LLMs. We distinguish population level cultural alignment from profile-level behavioural variation. Although several models approximate the human population-level distribution, all evaluated systems exhibit substantially lower variation across demographic and household profiles than the human cohort, revealing a gap between aggregate alignment and profile-conditioned sensitivity. As a downstream application of MH-INDIC, we use the strongest-aligned proprietary and open-source models to generate culturally conditioned maternal-health dialogues under zero-shot, self-conditioned, and human-grounded prompting. Human-grounded conditioning produces stronger profile alignment and dialogue quality ratings, suggesting that measured cultural profiles can improve the cultural grounding of generated interactions
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.11586 [cs.CL] |
| (or arXiv:2610.11586v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.11586 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Umaira Izhar [view email]
[v1]
Thu, 8 Oct 2026 09:35:15 UTC (3,088 KB)
来源：arXiv：cs.CL · arxiv.org