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) are applied 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 proposed Subcultural Alignment Solver (SAS), a multi-agent framework that incorporates automatic retrieval and subculture alignment, significantly enhancing 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.

翻译：自我毁灭行为与复杂的心理状态相关，且诊断难度较高。在亚文化群体中，由于表达方式独特，这些行为可能更难被识别。随着大型语言模型（LLMs）在各领域的应用，已有研究者开始探索其在自我毁灭行为检测中的应用。受此启发，本研究采用当前基于LLM的方法对亚文化中的自我毁灭行为进行检测。然而，现有方法面临两大挑战：（1）知识滞后：亚文化俚语演变速度快于LLM的训练周期；（2）语义错位：难以准确把握亚文化特有的细微表达。为解决这些问题，我们提出了亚文化对齐求解器（SAS）——一个融合自动检索与亚文化对齐的多智能体框架，显著提升了LLM在自我毁灭行为检测中的性能。实验结果表明，SAS优于当前先进的多智能体框架OWL，且与经过微调的LLM相比具有竞争力。我们希望SAS能推动亚文化背景下自我毁灭行为检测领域的发展，并为未来研究者提供有价值的参考资源。