Recent work has made a preliminary attempt to use large language models (LLMs) to solve the stance detection task, showing promising results. However, considering that stance detection usually requires detailed background knowledge, the vanilla reasoning method may neglect the domain knowledge to make a professional and accurate analysis. Thus, there is still room for improvement of LLMs reasoning, especially in leveraging the generation capability of LLMs to simulate specific experts (i.e., multi-agents) to detect the stance. In this paper, different from existing multi-agent works that require detailed descriptions and use fixed experts, we propose a Dynamic Experienced Expert Modeling (DEEM) method which can leverage the generated experienced experts and let LLMs reason in a semi-parametric way, making the experts more generalizable and reliable. Experimental results demonstrate that DEEM consistently achieves the best results on three standard benchmarks, outperforms methods with self-consistency reasoning, and reduces the bias of LLMs.

翻译：近期研究已初步尝试利用大型语言模型（LLMs）解决立场检测任务，并展现出令人期待的结果。然而，鉴于立场检测通常需要详尽的背景知识，常规推理方法可能忽视领域知识导致无法进行专业精准的分析。因此，LLMs的推理能力仍有提升空间，特别是如何利用LLMs的生成能力模拟特定专家（即多智能体）进行立场检测。本文提出一种动态经验型专家建模（DEEM）方法，不同于现有需要详细描述并采用固定专家的多智能体工作，该方法能利用生成的经验型专家，以半参数化方式驱动LLMs进行推理，从而增强专家的泛化性与可靠性。实验结果表明，DEEM在三个标准基准测试上持续取得最优结果，不仅优于采用自洽性推理的方法，还能有效降低LLMs的偏差。