Kavli Affiliate: Dan Luo
| First 5 Authors: Hongzhi Qi, Qing Zhao, Changwei Song, Wei Zhai, Dan Luo
| Summary:
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.
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