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Understanding User Intent in Code-Mixed Sexual and Reproductive Health Queries in Urban India: Hierarchical

Sumon Kanti Dey1, Manvi S1, Aradhana Thapa2

  • 1Department of Biomedical Informatics, School of Medicine, Emory University, 101 Woodruff Circle, Atlanta, GA, 30322, United States, 1 4046437205.

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Summary

Large language models (LLMs) show promise for sexual and reproductive health (SRH) queries in Hinglish, but struggle with nuanced, code-mixed language. Indic LLMs like Sarvam-M offer competitive performance, highlighting the need for culturally adapted AI.

Keywords:
Hinglishcode-mixingconversational agentshierarchical classificationlarge language modelssexual and reproductive health

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Area of Science:

  • Artificial Intelligence
  • Natural Language Processing
  • Global Health

Background:

  • Sexual and reproductive health (SRH) information access is limited globally due to stigma and taboo topics.
  • Linguistic and cultural diversity in the Global South, particularly India, complicates SRH information access.
  • Code-mixed languages like Hinglish and colloquial terms are common in SRH queries, posing challenges for standard English-trained LLMs.

Purpose of the Study:

  • To evaluate the effectiveness of proprietary, multilingual open-weight, and Indic LLMs in understanding user intent in code-mixed Hinglish SRH queries.
  • To assess LLM performance in a hierarchical classification of SRH topics and subtopics.
  • To identify common error types in LLM misclassification of SRH queries.

Main Methods:

  • Analysis of 4161 deidentified Hinglish SRH questions from an underserved community in urban Mumbai.
  • Hierarchical annotation of queries into 8 topics and 40 subtopics, capturing linguistic and cultural variations.
  • Zero-shot evaluation of proprietary, multilingual open-weight, and Indic LLMs using hierarchical F1, Exact Match, and accuracy metrics.

Main Results:

  • Proprietary models, particularly GPT-5 (hF1=0.784), performed best.
  • Indic LLM Sarvam-M (hF1=0.757) showed strong performance among open-weight models, comparable to leading multilingual systems.
  • Models struggled with fine-grained intent recognition, especially for euphemisms and culturally specific queries, indicating gaps in handling code-mixed language.

Conclusions:

  • Hierarchical classification reveals LLM limitations with code-mixed SRH queries.
  • Open-weight Indic systems like Sarvam-M demonstrate potential for high performance with adequate data and cultural adaptation.
  • Culturally aligned, localized models are crucial for advancing inclusive AI and equitable SRH information access globally.