Summary of Sample-efficient Alignment For Llms, by Zichen Liu et al.
Sample-Efficient Alignment for LLMs
by Zichen Liu, Changyu Chen, Chao Du, Wee Sun Lee, Min Lin
First submitted to arxiv on: 3 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 method called Sample-Efficient Alignment (SEA) that efficiently aligns large language models (LLMs) with human preferences using budgeted online feedback. The authors formulate the problem as a contextual dueling bandits framework, which allows for sample-efficient algorithms that incorporate online active exploration. They introduce a unified algorithm based on Thompson sampling and demonstrate its applications in two distinct LLM alignment scenarios. The results show that SEA achieves highly sample-efficient alignment with oracle’s preferences, outperforming recent active exploration methods for LLLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to teach an AI system what humans like and don’t like. This paper shows a new way to do this called Sample-Efficient Alignment (SEA). The idea is to use online feedback from humans to help the AI learn what’s important. The researchers used a special kind of math problem called contextual dueling bandits to figure out how to make the AI learn quickly and efficiently. They tested their method on three different AI models and showed that it works well. |
Keywords
» Artificial intelligence » Alignment
