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Psy-LLM: Scaling up Global Mental Health Psychological Services with AI-based Large Language Models

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arxiv 2307.11991 v2 pith:ABMI3Y4V submitted 2023-07-22 cs.CL cs.AI

classification cs.CLcs.AI
keywords psychologicalframeworkhealthmentalpsy-llmdemandlanguagelarge
verification ladder T0 review T1 audit T2 compute T3 formal
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The demand for psychological counselling has grown significantly in recent years, particularly with the global outbreak of COVID-19, which has heightened the need for timely and professional mental health support. Online psychological counselling has emerged as the predominant mode of providing services in response to this demand. In this study, we propose the Psy-LLM framework, an AI-based assistive tool leveraging Large Language Models (LLMs) for question-answering in psychological consultation settings to ease the demand for mental health professions. Our framework combines pre-trained LLMs with real-world professional Q\&A from psychologists and extensively crawled psychological articles. The Psy-LLM framework serves as a front-end tool for healthcare professionals, allowing them to provide immediate responses and mindfulness activities to alleviate patient stress. Additionally, it functions as a screening tool to identify urgent cases requiring further assistance. We evaluated the framework using intrinsic metrics, such as perplexity, and extrinsic evaluation metrics, with human participant assessments of response helpfulness, fluency, relevance, and logic. The results demonstrate the effectiveness of the Psy-LLM framework in generating coherent and relevant answers to psychological questions. This article discusses the potential and limitations of using large language models to enhance mental health support through AI technologies.

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Cited by 2 Pith papers

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  1. Scaling behavior of large language models in emotional safety classification across sizes and tasks

    cs.CL 2025-09 conditional novelty 4.0 of 10

    Fine-tuning a 1B LLaMA model on a synthetic emotional-safety benchmark matches or beats 70B few-shot performance and a BERT baseline on three high-data categories, using under 2GB VRAM.

  2. Blockchain Network Analysis using Quantum Inspired Graph Neural Networks & Ensemble Models

    cs.LG 2025-08 unverdicted novelty 4.0 of 10

    The submission's abstract claims a quantum-inspired GNN with a CP-decomposition layer reaches 74.8% F2 on blockchain fraud detection, but the uploaded full text is an unrelated paper on VLM agent security.

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