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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.
The model structures are shown in the following figure.
Comparison with other methods
Method
Including Explanations
Multi-Roles
Verified Experts
Reasoning Type
Few-Shot
β
β
-
Gen
CoT
β
β
-
Gen
Auto-CoT
β
β
-
Re+Gen
ExpertPrompt
β
β
β
Gen
SPP
β
β
β
Gen
DEEM(ours)
β
β
β
Re+Gen
Casestudy
Reference
π If you find our project helpful to your research, please consider citing:
@misc{wang2024deem,
title={DEEM: Dynamic Experienced Expert Modeling for Stance Detection},
author={Xiaolong Wang and Yile Wang and Sijie Cheng and Peng Li and Yang Liu},
year={2024},
eprint={2402.15264},
archivePrefix={arXiv},
primaryClass={cs.CL}
}