# Large Language Models for Mental Health: A Multilingual Evaluation

arXiv: https://arxiv.org/abs/2602.02440 · HTML: https://mlsanity.com/paper/2602.02440/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 50 references that are in the map.
- First posted on arXiv 2 Feb 2026.

## Builds on (most cited first, 16 of 16)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Gemma 2: Improving Open Language Models at a Practical Size](https://mlsanity.com/paper/2408.00118/) (2024)
- [Publicly Available Clinical BERT Embeddings](https://mlsanity.com/paper/1904.03323/) (2019)
- [Better to Ask in English: Cross-Lingual Evaluation of Large Language Models for Healthcare Queries](https://mlsanity.com/paper/2310.13132/) (2023)
- [Dreaddit: A Reddit Dataset for Stress Analysis in Social Media](https://mlsanity.com/paper/1911.00133/) (2019)
- [mHumanEval -- A Multilingual Benchmark to Evaluate Large Language Models for Code Generation](https://mlsanity.com/paper/2410.15037/) (2024)
- [Multilingual Multimodal Learning with Machine Translated Text](https://mlsanity.com/paper/2210.13134/) (2022)
- [Better Alignment with Instruction Back-and-Forth Translation](https://mlsanity.com/paper/2408.04614/) (2024)
- [Exploring the Efficacy of Large Language Models in Summarizing Mental Health Counseling Sessions: A Benchmark Study](https://mlsanity.com/paper/2402.19052/) (2024)
- [Diverse Perspectives, Divergent Models: Cross-Cultural Evaluation of Depression Detection on Twitter](https://mlsanity.com/paper/2406.15362/) (2024)
- [Detecting Depression in Thai Blog Posts: a Dataset and a Baseline](https://mlsanity.com/paper/2111.04574/) (2021)
- [On the Economics of Multilingual Few-shot Learning: Modeling the Cost-Performance Trade-offs of Machine Translated and Manual Data](https://mlsanity.com/paper/2205.06350/) (2022)
- [A Comprehensive Evaluation of Large Language Models on Mental Illnesses in Arabic Context](https://mlsanity.com/paper/2501.06859/) (2025)
- [MasonPerplexity at Multimodal Hate Speech Event Detection 2024: Hate Speech and Target Detection Using Transformer Ensembles](https://mlsanity.com/paper/2402.01967/) (2024)
- [Leveraging LLMs for Translating and Classifying Mental Health Data](https://mlsanity.com/paper/2410.12985/) (2024)
- [The First Multilingual Model For The Detection of Suicide Texts](https://mlsanity.com/paper/2412.15498/) (2024)

## Built on by (most cited first, 1 of 1)

- [Creating Multilingual Mental Health Dialogue Datasets: Limits of Persona-Based Localization via Nationality and Language](https://mlsanity.com/paper/2606.19640/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) have remarkable capabilities across NLP tasks. However, their performance in multilingual contexts, especially within the mental health domain, has not been thoroughly explored. In this paper, we evaluate proprietary and open-source LLMs on eight mental health datasets in various languages, as well as their machine-translated (MT) counterparts. We compare LLM performance in zero-shot, few-shot, and fine-tuned settings against conventional NLP baselines that do not employ LLMs. In addition, we assess translation quality across language families and typologies to understand its influence on LLM performance. Proprietary LLMs and fine-tuned open-source LLMs achieve competitive F1 scores on several datasets, often surpassing state-of-the-art results. However, performance on MT data is generally lower, and the extent of this decline varies by language and typology. This variation highlights both the strengths of LLMs in handling mental health tasks in languages other than English and their limitations when translation quality introduces structural or lexical mismatches.