mlsanityData to 14 Oct 2026

P-ReMIS: Pragmatic Reasoning in Mental Health and a Social Implication

arXiv 2507.23247 ↗First posted 31 Jul 2025cs.CLcited by 1

On the map: LLM reasoning → Clinical and biomedical applications

Abstract (arXiv)

Although explainability and interpretability have received significant attention in artificial intelligence (AI) and natural language processing (NLP) for mental health, reasoning has not been examined in the same depth. Addressing this gap is essential to bridge NLP and mental health through interpretable and reasoning-capable AI systems. To this end, we investigate the pragmatic reasoning capability of large-language models (LLMs) in the mental health domain. We introduce PRiMH dataset, and propose pragmatic reasoning tasks in mental health with pragmatic implicature and presupposition phenomena. In particular, we formulate two tasks in implicature and one task in presupposition. To benchmark the dataset and the tasks presented, we consider four models: Llama3.1, Mistral, MentaLLaMa, and Qwen. The results of the experiments suggest that Mistral and Qwen show substantial reasoning abilities in the domain. Subsequently, we study the behavior of MentaLLaMA on the proposed reasoning tasks with the rollout attention mechanism. In addition, we also propose three StiPRompts to study the stigma around mental health with the state-of-the-art LLMs, GPT4o-mini, Deepseek-chat, and Claude-3.5-haiku. Our evaluated findings show that Claude-3.5-haiku deals with stigma more responsibly compared to the other two LLMs.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Clinical and biomedical applications, one of 17 directions in LLM reasoning. Builds on 14 of its 30 references that are in the map. First posted on arXiv 31 Jul 2025.

Builds on

14 of 30 references.

References · 14

  1. 2023GPT-4 Technical Reportcited by 18,396
  2. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
  3. 2023Qwen Technical Reportcited by 3,344
  4. 2023Mistral 7Bcited by 3,164
  5. 2024DeepSeek-V3 Technical Reportcited by 811
  6. 2020Quantifying Attention Flow in Transformerscited by 775
  7. 2017Depression and Self-Harm Risk Assessment in Online Forumscited by 84
  8. 2024PUB: A Pragmatics Understanding Benchmark for Assessing LLMs' Pragmatics Capabilitiescited by 59
  9. 2022(QA)2: Question Answering with Questionable Assumptionscited by 51
  10. 2023Psy-LLM: Scaling up Global Mental Health Psychological Services with AI-based Large Language Modelscited by 36
  11. 2022CAMS: An Annotated Corpus for Causal Analysis of Mental Health Issues in Social Media Postscited by 34
  12. 2020On the State of Social Media Data for Mental Health Researchcited by 32
2 more
  1. 2025Pragmatics in the Era of Large Language Models: A Survey on Datasets, Evaluation, Opportunities and Challengescited by 31
  2. 2023NLP as a Lens for Causal Analysis and Perception Mining to Infer Mental Health on Social Mediacited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026From Pattern Recognizers to Personalized Companions: A Survey of Large Language Models in Mental Healthcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Cognitive-Mental-LLM: Evaluating Reasoning in Large Language Models for Mental Health Prediction via Online Text
  2. 2024A Dual-Prompting for Interpretable Mental Health Language Models
  3. 2025MiraMind: Benchmarking Reliable Mental Health Reasoning beyond Answer Accuracy
  4. 2023Towards Interpretable Mental Health Analysis with Large Language Models
  5. 2026Reasoning Over Recall: Evaluating the Efficacy of Generalist Architectures vs. Specialized Fine-Tunes in RAG-Based Mental Health Dialogue Systems
  6. 2026Analyzing LLM Reasoning to Uncover Mental Health Stigma
  7. 2025MindEval: Benchmarking Language Models on Multi-turn Mental Health Support
  8. 2026OMIND: Framework for Knowledge Grounded Finetuning and Multi-Turn Dialogue Benchmark for Mental Health LLMs