Summary of Alma: Alignment with Minimal Annotation, by Michihiro Yasunaga et al.
ALMA: Alignment with Minimal Annotation
by Michihiro Yasunaga, Leonid Shamis, Chunting Zhou, Andrew Cohen, Jason Weston, Luke Zettlemoyer, Marjan Ghazvininejad
First submitted to arxiv on: 5 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 A novel approach to large language model (LLM) alignment is introduced in this paper, which achieves effective alignment using only a minimal amount of human annotations. Dubbed ALMA (Alignment with Minimal Annotation), the method generates high-quality synthetic alignment data through diverse prompt synthesis, response generation, and judge enhancement techniques. The authors demonstrate that ALMA can achieve performance comparable to Llama3-Instruct across various alignment benchmarks using as little as 9,000 labeled examples – a fraction of conventional approaches. This is achieved through a multi-round self-bootstrapped data synthesis and training recipe that continues to improve for 10 rounds, surpassing the typical 3-round ceiling of previous methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to align large language models using very little human help. ALMA generates lots of fake data that is useful for alignment, which can be done with just 9,000 labeled examples – much less than usual. The authors use special techniques like making prompts diverse and having multiple models work together to create good fake data. They show that this approach works well, even beating some other methods that use more human help. |
Keywords
» Artificial intelligence » Alignment » Large language model » Prompt
