用微调版GPT-4o识别社交媒体自杀风险，效果优于其他模型。
Evaluating Transformer Models for Suicide Risk Detection on Social Media
- 微调GPT-4o模型，直接用于四类自杀风险分类。
- 在比赛中获第二名，准确率显著高于DeBERTa和CoT提示方案。
- 证明通用大模型经少量调优即可高效检测自杀风险，适合快速部署。
社交媒体中的自杀风险检测是一项具有潜在救命意义的关键任务。本文由kubapok团队提交至IEEE BigData 2024杯：社交媒体自杀风险检测竞赛，研究基于先进自然语言处理技术识别社交平台内容中的自杀风险。实验对比了三种基于Transformer的模型配置：微调后的DeBERTa、使用思维链（CoT）与少样本提示的GPT-4o，以及微调后的GPT-4o。任务目标是将社交媒体帖子分为四类：征兆、意念、行为和企图。结果表明，微调后的GPT-4o模型性能最优，在比赛中获得第二名。研究证明，仅通过简单调优的通用大模型即可实现顶尖表现，提示此类方法可能成为自动化自杀风险检测的有效解决方案。
原文摘要 · Abstract (English)
The detection of suicide risk in social media is a critical task with potential life-saving implications. This paper presents a study on leveraging state-of-the-art natural language processing solutions for identifying suicide risk in social media posts as a submission for the "IEEE BigData 2024 Cup: Detection of Suicide Risk on Social Media" conducted by the kubapok team. We experimented with the following configurations of transformer-based models: fine-tuned DeBERTa, GPT-4o with CoT and few-shot prompting, and fine-tuned GPT-4o. The task setup was to classify social media posts into four categories: indicator, ideation, behavior, and attempt. Our findings demonstrate that the fine-tuned GPT-4o model outperforms two other configurations, achieving high accuracy in identifying suicide risk. Notably, our model achieved second place in the competition. By demonstrating that straightforward, general-purpose models can achieve state-of-the-art results, we propose that these models, combined with minimal tuning, may have the potential to be effective solutions for automated suicide risk detection on social media.
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