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In the realm of social media, users frequently convey personal sentiments,with some potentially indicating cognitive distortions or suicidal tendencies.Timely recognition of such signs is pivotal for effective interventions. Inresponse, we introduce two novel annotated datasets from Chinese social media,focused on cognitive distortions and suicidal risk classification. We propose acomprehensive benchmark using both supervised learning and large languagemodels, especially from the GPT series, to evaluate performance on thesedatasets. To assess the capabilities of the large language models, we employedthree strategies: zero-shot, few-shot, and fine-tuning. Furthermore, we deeplyexplored and analyzed the performance of these large language models from apsychological perspective, shedding light on their strengths and limitations inidentifying and understanding complex human emotions. Our evaluationsunderscore a performance difference between the two approaches, with the modelsoften challenged by subtle category distinctions. While GPT-4 consistentlydelivered strong results, GPT-3.5 showed marked improvement in suicide riskclassification after fine-tuning. This research is groundbreaking in itsevaluation of large language models for Chinese social media tasks,accentuating the models' potential in psychological contexts. All datasets andcode are made available.

Supervised Learning and Large Language Model Benchmarks on Mental Health Datasets: Cognitive Distortions and Suicidal Risks in Chinese Social Media