PRiME: Pragmatic Reasoning In MEntal health

Abstract:In this work, the task and domain interaction of reasoning and mental health are investigated. As large language models (LLMs) present exceptional text understanding and fluent text generation capability, we analyze whether they can reason in the high-stakes domain of mental health. We begin by introducing three datasets and three tasks leveraging the existing work in pragmatic reasoning. The analysis constitutes an evaluation of $12$ LLMs, including MentaLLaMA, which is a mental health reasoning and explanation generation model. The performance of the models remains on par with human evaluation, with the Mistral-7B model giving the best performance. We further analyze the performance of the models with error asymmetry ratio and conduct first-order attention rollout experiment with MentaLLaMA-7B. We present the results for the community reporting the architectural behaviour of the models for the input text and recommend further careful analysis for the high-stakes domain.

Computation and Language (cs.CL)

arXiv:2507.23247 [cs.CL]

(or arXiv:2507.23247v3 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2507.23247 Focus to learn more arXiv-issued DOI via DataCite

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