Summary of Macpo: Weak-to-strong Alignment Via Multi-agent Contrastive Preference Optimization, by Yougang Lyu et al.
MACPO: Weak-to-Strong Alignment via Multi-Agent Contrastive Preference Optimization
by Yougang Lyu, Lingyong Yan, Zihan Wang, Dawei Yin, Pengjie Ren, Maarten de Rijke, Zhaochun Ren
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 This research proposes a multi-agent contrastive preference optimization (MACPO) framework to align large language models (LLMs) with human values in scenarios where LLMs outperform humans. The authors focus on the “weak-to-strong” alignment problem, where strong student LLMs need to be aligned through weak supervision generated by weak teachers. The MACPO framework iteratively reinforces unfamiliar positive behaviors while penalizing familiar negative ones, allowing weak teachers and strong students to learn from each other. To improve alignment performance, the authors also introduce a mutual positive behavior augmentation strategy and a hard negative behavior construction strategy. Experimental results on two datasets demonstrate that MACPO improves both strong student and weak teacher alignment performances. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are getting really good at doing certain tasks, but they’re not always aligned with human values. This means we need to find ways to make them work better together. The authors of this paper propose a new way to do this called MACPO. MACPO helps strong and weak models learn from each other by focusing on positive behaviors and avoiding negative ones. They also show how to get the most out of this approach by using two special strategies. By testing their ideas on real datasets, they found that MACPO really works well and can make both strong and weak models work better together. |
Keywords
» Artificial intelligence » Alignment » Optimization
