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BibTeX formatted
@misc{wang2024peerexpertizingdomainspecifictasks,
title={PEER: Expertizing Domain-Specific Tasks with a Multi-Agent Framework and Tuning Methods},
author={Yiying Wang and Xiaojing Li and Binzhu Wang and Yueyang Zhou and Han Ji and Hong Chen and Jinshi Zhang and Fei Yu and Zewei Zhao and Song Jin and Renji Gong and Wanqing Xu},
year={2024},
eprint={2407.06985},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2407.06985},
}
Overview: In the experimental section of this study, scores were assigned across seven dimensions: completeness, relevance, conciseness, factualness, logicality, structure, and comprehensiveness (each dimension has a maximum score of 5 points). The PEER model scored higher on average in each evaluation dimension compared to BabyAGI, and demonstrated significant advantages in the dimensions of completeness, relevance, logicality, structure, and comprehensiveness. Additionally, the PEER model achieved an 83% superior rate over BabyAGI using the GPT-3.5 turbo (16k) model, and an 81% superior rate using the GPT-4o model. For more details, please refer to the literature. https://arxiv.org/pdf/2407.06985