Summary of Role-rl: Online Long-context Processing with Role Reinforcement Learning For Distinct Llms in Their Optimal Roles, by Lewei He et al.
Role-RL: Online Long-Context Processing with Role Reinforcement Learning for Distinct LLMs in Their Optimal Roles
by Lewei He, Tianyu Shi, Pengran Huang, Bingzhi Chen, Qianglong Chen, Jiahui Pan
First submitted to arxiv on: 26 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 proposed Online Long-context Processing (OLP) paradigm addresses the challenges of large language models (LLMs) with long-context processing, particularly in information reception and organization of diverse streaming media. To achieve this, OLP is designed to process documents of unlimited length, leveraging LLMs for tasks like automated news reporting, live e-commerce, and viral short videos. The paper also presents Role Reinforcement Learning (Role-RL), which automatically deploys different LLLs in their respective roles within the OLP pipeline according to their actual performance. Extensive experiments on the OLP-MINI dataset demonstrate that the OLP with Role-RL framework achieves an average recall rate of 93.2% and saves LLM costs by 79.4%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way for computers to process large amounts of information from different sources, like news reports or live videos. This is important because it helps machines understand and organize diverse types of data more efficiently. The authors also developed a system that automatically chooses the best language model for the job, based on how well it performs. The results show that this new approach can process large amounts of information with high accuracy and save costs. |
Keywords
» Artificial intelligence » Language model » Recall » Reinforcement learning
