Summary of Meta-rewarding Language Models: Self-improving Alignment with Llm-as-a-meta-judge, by Tianhao Wu et al.
Meta-Rewarding Language Models: Self-Improving Alignment with LLM-as-a-Meta-Judge
by Tianhao Wu, Weizhe Yuan, Olga Golovneva, Jing Xu, Yuandong Tian, Jiantao Jiao, Jason Weston, Sainbayar Sukhbaatar
First submitted to arxiv on: 28 Jul 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 Recent advancements in Large Language Models (LLMs) have led to a rapid increase in their knowledge capabilities across various domains. Traditionally, improving these models relies on costly human data; however, self-rewarding mechanisms have shown that LLMs can improve by judging their own responses instead of relying on human labelers. This approach has primarily focused on enhancing model responses rather than judgment capabilities, resulting in rapid saturation during iterative training. To address this issue, we propose a novel Meta-Rewarding step to the self-improvement process, where the model judges its own judgments and uses that feedback to refine its judgment skills. Our results demonstrate a significant improvement in the model’s ability to judge and follow instructions, as seen on AlpacaEval 2 with an increase from 22.9% to 39.4% win rate for Llama-3-8B-Instruct, and on Arena-Hard with an increase from 20.6% to 29.1%. These findings suggest the potential for self-improving models without human supervision. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models are getting very good at understanding many things. Usually, we need people to help them get better, but a new way has been found where they can improve by judging their own answers instead. However, this approach has mainly focused on making the model’s answers better rather than how well it judges things itself. To fix this, we came up with a new idea called Meta-Rewarding that helps the model improve its judgment skills too. We tested our idea and found that it makes the model much better at following instructions and judging things correctly. For example, one of our models went from being right 22.9% of the time to 39.4%, which is a big improvement. These results show that computers can get even smarter without needing human help. |
Keywords
» Artificial intelligence » Llama
