Summary of Weak-to-strong Search: Align Large Language Models Via Searching Over Small Language Models, by Zhanhui Zhou et al.
Weak-to-Strong Search: Align Large Language Models via Searching over Small Language Models
by Zhanhui Zhou, Zhixuan Liu, Jie Liu, Zhichen Dong, Chao Yang, Yu Qiao
First submitted to arxiv on: 29 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 paper introduces a novel method, called weak-to-strong search, to fine-tune large language models without direct tuning. The approach frames alignment as a test-time greedy search, maximizing log-probability differences between small tuned and untuned models while sampling from the frozen large model. This technique serves both as a compute-efficient model up-scaling strategy and an instance of weak-to-strong generalization that enhances strong models with weak test-time guidance. Experimental results demonstrate the flexibility of weak-to-strong search across different tasks, including controlled-sentiment generation, summarization, and instruction-following benchmark AlpacaEval 2.0. The paper shows that reusing off-the-shelf small models can improve the length-controlled win rates of large models against gpt-4-turbo. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research develops a new way to adjust big language models without a lot of computation. It’s called weak-to-strong search, and it helps big models understand human preferences better. The method is like a test where the model tries different approaches to get closer to what humans want. This technique makes it easier to use big models for tasks like generating text or summarizing information. The study shows that using small models can help improve the performance of big models in certain situations. |
Keywords
» Artificial intelligence » Alignment » Generalization » Gpt » Probability » Summarization
