Severity Prediction in Mental Health: LLM-based Creation, Analysis, Evaluation of a Novel Multilingual Dataset

• URL: http://arxiv.org/abs/2409.17397v1
• Date: Wed, 25 Sep 2024 22:14:34 GMT
• Title: Severity Prediction in Mental Health: LLM-based Creation, Analysis, Evaluation of a Novel Multilingual Dataset
• Authors: Konstantinos Skianis, John Pavlopoulos, A. Seza Do\u{g}ru\"oz
• Abstract summary: Large Language Models (LLMs) are increasingly integrated into various medical fields, including mental health support systems. We present a novel multilingual adaptation of widely-used mental health datasets, translated from English into six languages. This dataset enables a comprehensive evaluation of LLM performance in detecting mental health conditions and assessing their severity across multiple languages.
• Score: 3.4146360486107987
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) are increasingly integrated into various medical fields, including mental health support systems. However, there is a gap in research regarding the effectiveness of LLMs in non-English mental health support applications. To address this problem, we present a novel multilingual adaptation of widely-used mental health datasets, translated from English into six languages (Greek, Turkish, French, Portuguese, German, and Finnish). This dataset enables a comprehensive evaluation of LLM performance in detecting mental health conditions and assessing their severity across multiple languages. By experimenting with GPT and Llama, we observe considerable variability in performance across languages, despite being evaluated on the same translated dataset. This inconsistency underscores the complexities inherent in multilingual mental health support, where language-specific nuances and mental health data coverage can affect the accuracy of the models. Through comprehensive error analysis, we emphasize the risks of relying exclusively on large language models (LLMs) in medical settings (e.g., their potential to contribute to misdiagnoses). Moreover, our proposed approach offers significant cost savings for multilingual tasks, presenting a major advantage for broad-scale implementation.

Related papers

• Do LLMs Provide Consistent Answers to Health-Related Questions across Languages? [14.87110905165928]
  We examine the consistency of responses provided by Large Language Models (LLMs) to health-related questions across English, German, Turkish, and Chinese. We reveal significant inconsistencies in responses that could spread healthcare misinformation. Our findings emphasize the need for improved cross-lingual alignment to ensure accurate and equitable healthcare information.
  arXiv  Detail & Related papers  (2025-01-24T18:51:26Z)
• Bridging Language Barriers in Healthcare: A Study on Arabic LLMs [1.2006896500048552]
  This paper investigates the challenges of developing large language models proficient in both multilingual understanding and medical knowledge. We find that larger models with carefully calibrated language ratios achieve superior performance on native-language clinical tasks.
  arXiv  Detail & Related papers  (2025-01-16T20:24:56Z)
• A Comprehensive Evaluation of Large Language Models on Mental Illnesses in Arabic Context [0.9074663948713616]
  Mental health disorders pose a growing public health concern in the Arab world. This study comprehensively evaluates 8 large language models (LLMs) on diverse mental health datasets.
  arXiv  Detail & Related papers  (2025-01-12T16:17:25Z)
• LlaMADRS: Prompting Large Language Models for Interview-Based Depression Assessment [75.44934940580112]
  This study introduces LlaMADRS, a novel framework leveraging open-source Large Language Models (LLMs) to automate depression severity assessment. We employ a zero-shot prompting strategy with carefully designed cues to guide the model in interpreting and scoring transcribed clinical interviews. Our approach, tested on 236 real-world interviews, demonstrates strong correlations with clinician assessments.
  arXiv  Detail & Related papers  (2025-01-07T08:49:04Z)
• Multi-OphthaLingua: A Multilingual Benchmark for Assessing and Debiasing LLM Ophthalmological QA in LMICs [3.1894617416005855]
  Large language models (LLMs) present a promising solution to automate various ophthalmology procedures. LLMs have demonstrated significantly varied performance across different languages in natural language question-answering tasks. This study introduces the first multilingual ophthalmological question-answering benchmark with manually curated questions parallel across languages.
  arXiv  Detail & Related papers  (2024-12-18T20:18:03Z)
• XTransplant: A Probe into the Upper Bound Performance of Multilingual Capability and Culture Adaptability in LLMs via Mutual Cross-lingual Feed-forward Transplantation [49.69780199602105]
  Current large language models (LLMs) often exhibit imbalances in multilingual capabilities and cultural adaptability. We propose a probing method named XTransplant that explores cross-lingual latent interactions via cross-lingual feed-forward transplantation. We empirically prove that both the multilingual capabilities and cultural adaptability of LLMs hold the potential to be significantly improved by XTransplant.
  arXiv  Detail & Related papers  (2024-12-17T09:05:30Z)
• A Survey on Large Language Models with Multilingualism: Recent Advances and New Frontiers [51.8203871494146]
  The rapid development of Large Language Models (LLMs) demonstrates remarkable multilingual capabilities in natural language processing. Despite the breakthroughs of LLMs, the investigation into the multilingual scenario remains insufficient. This survey aims to help the research community address multilingual problems and provide a comprehensive understanding of the core concepts, key techniques, and latest developments in multilingual natural language processing based on LLMs.
  arXiv  Detail & Related papers  (2024-05-17T17:47:39Z)
• The Power of Question Translation Training in Multilingual Reasoning: Broadened Scope and Deepened Insights [108.40766216456413]
  We propose a question alignment framework to bridge the gap between large language models' English and non-English performance. Experiment results show it can boost multilingual performance across diverse reasoning scenarios, model families, and sizes. We analyze representation space, generated response and data scales, and reveal how question translation training strengthens language alignment within LLMs.
  arXiv  Detail & Related papers  (2024-05-02T14:49:50Z)
• Better to Ask in English: Cross-Lingual Evaluation of Large Language Models for Healthcare Queries [31.82249599013959]
  Large language models (LLMs) are transforming the ways the general public accesses and consumes information. LLMs demonstrate impressive language understanding and generation proficiencies, but concerns regarding their safety remain paramount. It remains unclear how these LLMs perform in the context of non-English languages.
  arXiv  Detail & Related papers  (2023-10-19T20:02:40Z)
• Few-Shot Cross-lingual Transfer for Coarse-grained De-identification of Code-Mixed Clinical Texts [56.72488923420374]
  Pre-trained language models (LMs) have shown great potential for cross-lingual transfer in low-resource settings. We show the few-shot cross-lingual transfer property of LMs for named recognition (NER) and apply it to solve a low-resource and real-world challenge of code-mixed (Spanish-Catalan) clinical notes de-identification in the stroke.
  arXiv  Detail & Related papers  (2022-04-10T21:46:52Z)
• AM2iCo: Evaluating Word Meaning in Context across Low-ResourceLanguages with Adversarial Examples [51.048234591165155]
  We present AM2iCo, Adversarial and Multilingual Meaning in Context. It aims to faithfully assess the ability of state-of-the-art (SotA) representation models to understand the identity of word meaning in cross-lingual contexts. Results reveal that current SotA pretrained encoders substantially lag behind human performance.
  arXiv  Detail & Related papers  (2021-04-17T20:23:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.