DEEM: Dynamic Experienced Expert Modeling for Stance Detection
Xiaolong Wang Yile Wang Sijie Cheng Peng Li Yang Liu
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.