HerHealthEval: Evaluating Multilingual and Register-Sensitive Understanding of Women's Health Communication
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Computer Science > Computation and Language
arXiv:2609.20684 (cs)
[Submitted on 17 Sep 2026]
Title:HerHealthEval: Evaluating Multilingual and Register-Sensitive Understanding of Women's Health Communication
Authors:Hassan Saeed Hassan Albattra, Mazen Mohammed Bahgat, Rahatara Ferdousi, Hana Essam Sayed Ahmed Amrya, Mariam Mousa
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Abstract:Large language models are increasingly used in healthcare communication, yet most evaluations emphasize response quality while assuming that the user's concern has been interpreted correctly. We introduce HerHealthEval, a controlled evaluation framework for multilingual understanding of women's-health communication. For each clinical case, HerHealthEval provides matched versions in English, French, and Modern Standard Arabic using six communicative forms: canonical, clinical, layperson, indirect or hedged, emotionally concerned, and deliberately under-specified. The first five express the same underlying concern and retain the same clinical information, whereas the under-specified form intentionally omits relevant details to test whether the model recognizes that clarification is needed. We evaluate a multilingual instruction model and QLoRA-adapted variants on concern classification, risk calibration, clarification behavior, parse compliance, and cross-form consistency. Results reveal that aggregate accuracy and consistency can conceal safety-relevant failures. A multilingual adaptation model reaches 0.994 under-triage in French and Arabic under language-asymmetric risk supervision. A controlled re-adaptation using source-derived, language-invariant risk labels reduces under-triage to 0.572 and 0.558, respectively. These findings show that robust multilingual healthcare evaluation requires explicit testing of register variation, uncertainty handling, and the provenance and invariance of adaptation labels.
| Comments: | 8 pages, 2 figures, 3 tables. Submitted to the 2026 International Conference on Large Language Models (LLM 2026) |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2609.20684 [cs.CL] |
| (or arXiv:2609.20684v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.20684
arXiv-issued DOI via DataCite (pending registration)
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