Summary of Peer: Expertizing Domain-specific Tasks with a Multi-agent Framework and Tuning Methods, by Yiying Wang et al.
PEER: Expertizing Domain-Specific Tasks with a Multi-Agent Framework and Tuning Methods
by Yiying Wang, Xiaojing Li, Binzhu Wang, Yueyang Zhou, Yingru Lin, Han Ji, Hong Chen, Jinshi Zhang, Fei Yu, Zewei Zhao, Song Jin, Renji Gong, Wanqing Xu
First submitted to arxiv on: 9 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The PEER multi-agent framework is introduced to address the challenges of high-performance, cost-effectiveness, and data privacy in domain-specific applications like financial question-answering. By integrating precise question decomposition, advanced information retrieval, comprehensive summarization, and rigorous self-assessment, the system achieves 95.0% of GPT-4’s performance while managing costs and ensuring data privacy. The framework combines plan, execute, express, and review processes to streamline domain-specific tasks. Industrial practices leveraging online data and user feedback are developed for efficient model tuning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The PEER multi-agent framework helps solve complex problems in specific areas like finance by breaking down questions, finding relevant info, summarizing results, and checking work. It’s better than GPT-4 at getting the right answers while being more cost-effective and private with data. This study shows how to use this approach in real-world applications. |
Keywords
» Artificial intelligence » Gpt » Question answering » Summarization
