标题： 可以大语言模型解决自我毁灭亚文化中的语义差异吗？ 来自Jirai Kei的证据 显示英文标题

标题： Can Large Language Models Resolve Semantic Discrepancy in Self-Destructive Subcultures? Evidence from Jirai Kei

摘要： 自毁行为与复杂的心理状态有关，且可能难以诊断。 这些行为在亚文化群体中可能更难被识别，因为它们有独特的表达方式。 随着大型语言模型（LLMs）在各个领域的应用，一些研究人员开始探索其在检测自毁行为方面的应用。 受此启发，我们研究了使用当前基于LLM的方法在亚文化中检测自毁行为。 然而，这些方法存在两个主要挑战：（1）知识滞后：亚文化俚语发展迅速，快于LLM的训练周期；（2）语义不一致：难以掌握亚文化特有的具体和细微的表达方式。 为了解决这些问题，我们提出了亚文化对齐求解器（SAS），这是一个包含自动检索和亚文化对齐的多智能体框架，显著提升了LLM在检测自毁行为方面的性能。 我们的实验结果表明，SAS优于当前先进的多智能体框架OWL。 值得注意的是，它与微调后的LLM表现相当。 我们希望SAS能够推动亚文化背景下自毁行为检测领域的发展，并作为未来研究人员的宝贵资源。 显示英文摘要

摘要： 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.