Summary of Cycleresearcher: Improving Automated Research Via Automated Review, by Yixuan Weng et al.
CycleResearcher: Improving Automated Research via Automated Review
by Yixuan Weng, Minjun Zhu, Guangsheng Bao, Hongbo Zhang, Jindong Wang, Yue Zhang, Linyi Yang
First submitted to arxiv on: 28 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (cs.LG)
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| 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 paper explores the feasibility of using open-source post-trained large language models (LLMs) as autonomous agents to automate the entire research process, from literature review and manuscript preparation to peer review and paper refinement. The authors develop an iterative preference training framework consisting of CycleResearcher, which conducts research tasks, and CycleReviewer, which simulates the peer review process, providing iterative feedback via reinforcement learning. Two new datasets are developed: Review-5k and Research-14k, reflecting real-world machine learning research and peer review dynamics. The results demonstrate that CycleReviewer achieves promising performance with a 26.89% reduction in mean absolute error (MAE) compared to individual human reviewers in predicting paper scores. In research, the papers generated by the CycleResearcher model achieved a score of 5.36 in simulated peer reviews, showing some competitiveness in terms of simulated review scores compared to the preprint level of 5.24 from human experts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using computers to help with scientific research. It wants to see if these computers can do all the work that humans do when they’re researching a topic, like reading and writing papers, and then having other people review their work. The computers are trained to do this by being given examples of good research and bad research, and then it’s tested to see how well it does. The results show that the computer can be pretty good at doing some parts of the job, but not as good as humans. |
Keywords
» Artificial intelligence » Machine learning » Mae » Reinforcement learning
