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Creating Multilingual Mental Health Dialogue Datasets: Limits of Persona-Based Localization via Nationality and Language

Paper · 2026 · Yunkai Xu and Saeed Abdullah

Summary

Synthetic clinical personas are increasingly used to build mental health dialogue data for training and testing LLM-based support systems. Most validated personas, however, are written for English-speaking, Western users. The authors ask whether such personas can be localized simply by changing two prompt parameters, nationality and output language, without losing their clinical content.

They began with clinically grounded English depression personas from Wang et al. (2025), which encode BDI-II severity, symptoms, communication style, social context and life history. Holding everything else fixed, they generated therapy-style dialogues in English, Mandarin, Bengali and Hindi. Several LLM judges, which never saw the persona prompts, then compared pairs of conversations to decide which one showed more severe depression. Their accuracy was checked against the intended severity ordering, using English as the baseline.

The results show that this minimal localization weakens and distorts the severity signal in non-English dialogues. Judge accuracy and certainty also differ widely across languages and models. The authors conclude that synthetic personas should be treated as clinical artifacts that need validation at the output level and evaluation specific to each language. This matters for building fair multilingual mental health tools, including tools for lower-resource languages.

Key points

  • The study uses 12 baseline depression personas adapted from Wang et al. (2025). Each persona has a BDI-II score and severity level and spans dimensions such as demographics, symptoms, communication patterns, social context and life history.

  • Two variables were added, Nationality and Language Use, paired as United States–English, Chinese–Mandarin, Bangladeshi–Bengali and Indian–Hindi. These pairs were chosen because the languages are among the most widely spoken worldwide.

  • Evaluation relied on blinded pairwise severity comparisons by different LLM judges. The outcomes were classified as correct, incorrect or tie, and tie frequency and tie distance served as descriptive measures of judge uncertainty.

  • Two main findings emerged:

    • Parameter-only localization produced clinical inconsistency across languages.
    • Judges were often inaccurate on non-English texts, with performance depending on the model.

  • The authors list several limitations:

    • No human clinician or native-speaker validation.
    • No statistical significance testing.
    • Language and culture are confounded through the nationality–language pairing.
    • Dialogues are short.

    The authors have made every persona and the complete pipeline openly available on GitHub. The paper appears in CLPsych 2026, pp. 138–152.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Pennsylvania State UniversityState College · United States67 works · 5,865 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Psychology · 1,649 worksExperimental and Cognitive Psychology · 930 worksSocial Psychology · 330 worksApplied Psychology · 25 works

🧭 Topics 2Research subjects and languages the work is about.Multilingual & cross-lingual NLP · 882 worksDialogue systems · 654 works

Cite

Cite as Yunkai Xu and Saeed Abdullah. 2026. Creating Multilingual Mental Health Dialogue Datasets: Limits of Persona-Based Localization via Nationality and Language. Proceedings of the 11th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2026).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.clpsych-1.11

@inproceedings{xu2026creating,
  title = {{Creating Multilingual Mental Health Dialogue Datasets: Limits of Persona-Based Localization via Nationality and Language}},
  author = {Yunkai Xu and Saeed Abdullah},
  booktitle = {Proceedings of the 11th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2026)},
  year = {2026},
  doi = {10.18653/v1/2026.clpsych-1.11},
  url = {https://doi.org/10.18653/v1/2026.clpsych-1.11}
}

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