Summary of Spread Preference Annotation: Direct Preference Judgment For Efficient Llm Alignment, by Dongyoung Kim et al.
Spread Preference Annotation: Direct Preference Judgment for Efficient LLM Alignment
by Dongyoung Kim, Kimin Lee, Jinwoo Shin, Jaehyung Kim
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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 The proposed framework, Spread Preference Annotation with direct preference judgment (SPA), aims to align large language models (LLMs) with human preferences while minimizing the need for a large human-annotated dataset. By leveraging prior knowledge within a small seed data and iteratively generating responses to learn from self-annotated preference data, SPA boosts alignment performance without requiring significant human annotation. The approach derives preference labels from LLM logits to extract inherent preferences, outperforming previous methods using external reward models or implicit in-context learning. Additionally, the framework introduces a noise-aware preference learning algorithm to mitigate risks of low-quality generated preference data. Experimental results demonstrate superior alignment performance on AlpacaEval 2.0 using only 3.3% of ground-truth labels compared to baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a big problem in language models! Right now, we need a lot of human help to make these models understand what humans like and dislike. But that takes a lot of time and effort. The scientists came up with a new way called SPA (Spread Preference Annotation) that only needs a little bit of human help to get the same results. They do this by using the model’s own ideas about what people might like, and then learning from those ideas. This approach is much better than previous methods, and it can even work with just a tiny amount of human input! The scientists tested their idea on a big dataset called AlpacaEval 2.0 and found that it worked really well. |
Keywords
» Artificial intelligence » Alignment » Logits
