Summary of Aligner: Efficient Alignment by Learning to Correct, By Jiaming Ji et al.
Aligner: Efficient Alignment by Learning to Correct
by Jiaming Ji, Boyuan Chen, Hantao Lou, Donghai Hong, Borong Zhang, Xuehai Pan, Juntao Dai, Tianyi Qiu, Yaodong Yang
First submitted to arxiv on: 4 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 This paper introduces Aligner, a novel alignment paradigm that learns correctional residuals between preferred and dispreferred answers using a small model. Designed as a model-agnostic, plug-and-play module, Aligner can be applied to various open-source and API-based models with only one-off training. The approach enables rapid iteration and can even iteratively bootstrap upstream models using corrected responses as synthetic human preference data. Experiments demonstrate performance improvements across 11 different large language models (LLMs), evaluated on the 3H dimensions of helpfulness, harmlessness, and honesty. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Aligner is a new way to improve large language models so they give better answers. It works by learning from small mistakes between good and bad responses. This helps the model learn faster and makes it more accurate. The researchers tested Aligner on many different big language models and found that it worked well, making their answers 69% better and reducing mistakes called hallucinations. |
Keywords
* Artificial intelligence * Alignment
