# Between Myths and Metaphors: Rethinking LLMs for SRH in Conservative Contexts

arXiv: https://arxiv.org/abs/2511.01907 · HTML: https://mlsanity.com/paper/2511.01907/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 76 references that are in the map.
- First posted on arXiv 31 Oct 2025.

## Builds on (most cited first, 13 of 13)

- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [The State and Fate of Linguistic Diversity and Inclusion in the NLP World](https://mlsanity.com/paper/2004.09095/) (2020)
- [Large language models in healthcare and medical domain: A review](https://mlsanity.com/paper/2401.06775/) (2023)
- [Mitigating Language-Dependent Ethnic Bias in BERT](https://mlsanity.com/paper/2109.05704/) (2021)
- [Do Multilingual LLMs Think In English?](https://mlsanity.com/paper/2502.15603/) (2025)
- [The opportunities and risks of large language models in mental health](https://mlsanity.com/paper/2403.14814/) (2024)
- [Self-Supervised Euphemism Detection and Identification for Content Moderation](https://mlsanity.com/paper/2103.16808/) (2021)
- [Euphemistic Phrase Detection by Masked Language Model](https://mlsanity.com/paper/2109.04666/) (2021)
- ["Kya family planning after marriage hoti hai?": Integrating Cultural Sensitivity in an LLM Chatbot for Reproductive Health](https://mlsanity.com/paper/2502.15939/) (2025)
- [Exploring Socio-Cultural Challenges and Opportunities in Designing Mental Health Chatbots for Adolescents in India](https://mlsanity.com/paper/2503.08562/) (2025)
- [Analysing Emergent Users' Text Messages Data and Exploring its Benefits](https://mlsanity.com/paper/1811.11983/) (2018)
- [Health literacy in e-oncology care: challenges and strategies](https://mlsanity.com/paper/2201.08718/) (2022)
- [`Socheton': A Culturally Appropriate AI Tool to Support Reproductive Well-being](https://mlsanity.com/paper/2506.12357/) (2025)

## Abstract (arXiv)

Low-resource countries represent over 90% of maternal deaths, with Pakistan among the top four countries contributing nearly half in 2023. Since these deaths are mostly preventable, large language models (LLMs) can help address this crisis by automating health communication and risk assessment. However, sexual and reproductive health (SRH) communication in conservative contexts often relies on indirect language that obscures meaning, complicating LLM-based interventions. We conduct a two-stage study in Pakistan: (1) analyzing data from clinical observations, interviews, and focus groups with clinicians and patients, and (2) evaluating the interpretive capabilities of five popular LLMs on this data. Our analysis identifies two axes of communication (referential domain and expression approach) and shows LLMs struggle with semantic drift, myths, and polysemy in clinical interactions. We contribute: (1) empirical themes in SRH communication, (2) a categorization framework for indirect communication, (3) evaluation of LLM performance, and (4) design recommendations for culturally-situated SRH communication.