Summary of Annotation-efficient Preference Optimization For Language Model Alignment, by Yuu Jinnai et al.
Annotation-Efficient Preference Optimization for Language Model Alignment
by Yuu Jinnai, Ukyo Honda
First submitted to arxiv on: 22 May 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 The paper proposes Annotation-Efficient Preference Optimization (AEPO), a method to optimize large language models for human preferences while minimizing the annotation budget. The quality, diversity, and quantity of preference annotations are crucial, but obtaining these is challenging in many applications. AEPO selects a subset of responses that maximizes quality and diversity from available texts, focusing the annotation budget on labeling high-quality and diverse preferences. This approach outperforms standard Direct Preference Optimization (DPO) with the same annotation budget. The authors evaluate AEPO using DPO and demonstrate its effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a big language model that can generate text, but it doesn’t always say what humans want to hear. To fix this, you need to teach the model what people like or dislike. This is called preference optimization. But getting lots of good quality examples for the model to learn from is hard. The authors of this paper came up with a new way to do preference optimization that uses less data and still gets good results. They call it Annotation-Efficient Preference Optimization (AEPO). AEPO picks the most important responses that will teach the model the most about what humans like or dislike, and then labels those responses. This approach works better than the usual way of doing things with the same amount of data. |
Keywords
» Artificial intelligence » Language model » Optimization
