Summary of Preference Consistency Matters: Enhancing Preference Learning in Language Models with Automated Self-curation Of Training Corpora, by Joonho Lee et al.
Preference Consistency Matters: Enhancing Preference Learning in Language Models with Automated Self-Curation of Training Corpora
by JoonHo Lee, JuYoun Son, Juree Seok, Wooseok Jang, Yeong-Dae Kwon
First submitted to arxiv on: 23 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 Inconsistent annotations in training corpora, particularly within preference learning datasets, hinder the development of advanced language models. Our self-curation method preprocesses annotated datasets by leveraging proxy models trained directly on them, enhancing preference learning by automatically detecting and selecting consistent annotations. We validate our approach through extensive instruction-following tasks, demonstrating performance improvements of up to 33% across various learning algorithms and proxy capabilities. This work offers a straightforward and reliable solution to address preference inconsistencies without relying on heuristics, serving as an initial step toward the development of more advanced preference learning methodologies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary When we try to teach computers about our preferences, we have a problem. Some people who helped create these training data might not agree with each other, making it hard for the computer to learn. To fix this, we came up with a new way to look at the data that helps us find the good parts and ignore the bad parts. We tested it and found that it made our computers better at following instructions by as much as 33%. This is an important step towards making computers understand what we like and dislike. |
