用多智能体框架解决亚文化自毁行为识别中的语义偏差问题
Can Large Language Models Resolve Semantic Discrepancy in Self-Destructive Subcultures? Evidence from Jirai Kei
- 设计多智能体系统自动检索并对齐亚文化表达
- 在Jirai Kei数据集上超越OWL框架，接近微调模型性能
- 适合心理健康监测与亚文化研究者使用
自毁行为与复杂心理状态相关，其在亚文化群体中因独特表达方式更难识别。尽管大语言模型（LLMs）已用于该领域检测，但仍面临两大挑战：（1）知识滞后——亚文化俚语更新速度超过模型训练周期；（2）语义错位——难以捕捉亚文化特有的细微表达。为此，我们提出亚文化对齐求解器（SAS），一个融合自动检索与亚文化对齐的多智能体框架，显著提升LLM在亚文化自毁行为检测中的表现。实验结果表明，SAS优于当前先进多智能体框架OWL，且在性能上可与微调后的LLM相媲美。我们期望SAS能推动亚文化背景下自毁行为检测的发展，并为后续研究提供重要资源。
原文摘要 · Abstract (English)
Self-destructive behaviors are linked to complex psychological states and can be challenging to diagnose. These behaviors may be even harder to identify within subcultural groups due to their unique expressions. As large language models (LLMs) being deployed across various fields, some researchers have begun exploring their application for detecting self-destructive behaviors. Motivated by this, we investigate self-destructive behavior detection within subcultures using current LLM-based methods. However, these methods have two main challenges: (1) Knowledge Lag: Subcultural slang evolves rapidly, faster than LLMs' training cycles; and (2) Semantic Misalignment: it is challenging to grasp the specific and nuanced expressions unique to subcultures. To address these issues, we propose Subcultural Alignment Solver (SAS), a multi-agent framework that incorporates automatic retrieval and subculture alignment, significantly boosting the performance of LLMs in detecting self-destructive behavior. Our experimental results show that SAS outperforms the current advanced multi-agent framework OWL. Notably, it competes well with fine-tuned LLMs. We hope that SAS will advance the field of self-destructive behavior detection in subcultural contexts and serve as a valuable resource for future researchers.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。