This study explores the use of Large Language Models (LLMs) to analyze text
comments from Reddit users, aiming to achieve two primary objectives: firstly,
to pinpoint critical excerpts that support a predefined psychological
assessment of suicidal risk; and secondly, to summarize the material to
substantiate the preassigned suicidal risk level. The work is circumscribed to
the use of "open-source" LLMs that can be run locally, thereby enhancing data
privacy. Furthermore, it prioritizes models with low computational
requirements, making it accessible to both individuals and institutions
operating on limited computing budgets. The implemented strategy only relies on
a carefully crafted prompt and a grammar to guide the LLM's text completion.
Despite its simplicity, the evaluation metrics show outstanding results, making
it a valuable privacy-focused and cost-effective approach. This work is part of
the Computational Linguistics and Clinical Psychology (CLPsych) 2024 shared
task.

该研究探讨使用大型语言模型（LLMs）分析 Reddit 用户的文本评论，旨在实现两个主要目标：首先，找出支持预定义心理评估自杀风险的关键摘录；其次，总结材料以证实预先分配的自杀风险水平。该研究仅限于在本地运行的 “开源” LLMs 的使用，从而增强数据隐私。此外，该研究优先考虑计算要求低的模型，使其可供具有有限计算预算的个人和机构使用。该实施策略仅依靠精心设计的提示和语法来指导 LLM 的文本补全。尽管简单，评估指标显示出卓越结果，使其成为一种重要的关注隐私和成本效益的方法。这项工作是 2024 年计算语言学和临床心理学（CLPsych）共享任务的一部分。

大型语言模型的心理评估：注重隐私且具有成本效益的方法

Psychological Assessments with Large Language Models: A Privacy-Focused  and Cost-Effective Approach

Large language models, particularly those akin to the rapidly progressing GPT
series, are gaining traction for their expansive influence. While there is keen
interest in their applicability within medical domains such as psychology,
tangible explorations on real-world data remain scant. Concurrently, users on
social media platforms are increasingly vocalizing personal sentiments; under
specific thematic umbrellas, these sentiments often manifest as negative
emotions, sometimes escalating to suicidal inclinations. Timely discernment of
such cognitive distortions and suicidal risks is crucial to effectively
intervene and potentially avert dire circumstances. Our study ventured into
this realm by experimenting on two pivotal tasks: suicidal risk and cognitive
distortion identification on Chinese social media platforms. Using supervised
learning as a baseline, we examined and contrasted the efficacy of large
language models via three distinct strategies: zero-shot, few-shot, and
fine-tuning. Our findings revealed a discernible performance gap between the
large language models and traditional supervised learning approaches, primarily
attributed to the models' inability to fully grasp subtle categories. Notably,
while GPT-4 outperforms its counterparts in multiple scenarios, GPT-3.5 shows
significant enhancement in suicide risk classification after fine-tuning. To
our knowledge, this investigation stands as the maiden attempt at gauging large
language models on Chinese social media tasks. This study underscores the
forward-looking and transformative implications of using large language models
in the field of psychology. It lays the groundwork for future applications in
psychological research and practice.

本研究旨在在中国社交媒体平台上评估大型语言模型对自杀风险和认知失真的识别能力，发现大型语言模型相较传统的监督学习方法存在较大的性能差距，尤其在微小类别的理解上，GPT-4 在多种情况下表现优异，而 GPT-3.5 经过微调后在自杀风险分类中显示出显著提升，表明大型语言模型在心理学领域具有前瞻性和变革性的应用潜力。

评估监督学习与大型语言模型对识别中国社交媒体中的认知扭曲和自杀风险的功效

Evaluating the Efficacy of Supervised Learning vs Large Language Models  for Identifying Cognitive Distortions and Suicidal Risks in Chinese Social  Media

With a surge in identifying suicidal risk and its severity in social media
posts, we argue that a more consequential and explainable research is required
for optimal impact on clinical psychology practice and personalized mental
healthcare. The success of computational intelligence techniques for inferring
mental illness from social media resources, points to natural language
processing as a lens for determining Interpersonal Risk Factors (IRF) in human
writings. Motivated with limited availability of datasets for social NLP
research community, we construct and release a new annotated dataset with
human-labelled explanations and classification of IRF affecting mental
disturbance on social media: (i) Thwarted Belongingness (TBe), and (ii)
Perceived Burdensomeness (PBu). We establish baseline models on our dataset
facilitating future research directions to develop real-time personalized AI
models by detecting patterns of TBe and PBu in emotional spectrum of user's
historical social media profile.

本研究构建并发布一个新的标注数据集，针对社交媒体上影响精神健康的人际风险因素进行分类和解释，以自然语言处理为切入点，建立模型并探讨以此为基础的 AI 个性化模型的未来研究方向。