Summary of Towards Federated Rlhf with Aggregated Client Preference For Llms, by Feijie Wu et al.
Towards Federated RLHF with Aggregated Client Preference for LLMs
by Feijie Wu, Xiaoze Liu, Haoyu Wang, Xingchen Wang, Lu Su, Jing Gao
First submitted to arxiv on: 3 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC); 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 In this paper, researchers propose a new approach to fine-tuning large language models using user feedback, enabling them to generate content aligned with human preferences. To address privacy concerns, they develop Federated Learning techniques that allow for large-scale preference collection without requiring data transmission to a central server. The proposed methods, FedBis and FedBiscuit, use binary selectors to capture common preferences and overcome challenges like preference heterogeneity and reward hacking. Experimental results show that these methods significantly improve the professionalism and readability of generated content. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding ways for computers to understand what people want them to say or write. To do this, researchers are using a type of learning called reinforcement learning, which helps computers generate text that people like. But there’s a problem: people might not want to share their personal preferences with the computer. To solve this, scientists developed a new way to collect preferences without sharing sensitive information. This new method is called Federated Learning and it lets many different people contribute their preferences without having to send their data to one central place. The researchers tested these new methods and found that they can make computers generate text that’s much more professional and easy to read. |
Keywords
* Artificial intelligence * Federated learning * Fine tuning * Reinforcement learning
