Summary of Post-edits Are Preferences Too, by Nathaniel Berger and Miriam Exel and Matthias Huck and Stefan Riezler
Post-edits Are Preferences Too
by Nathaniel Berger, Miriam Exel, Matthias Huck, Stefan Riezler
First submitted to arxiv on: 3 Oct 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 In this paper, researchers explore the challenges of fine-tuning large language models on machine translation tasks using preference optimization techniques. While these methods excel in leveraging human annotators’ pairwise preference feedback for other applications, they struggle to obtain sufficient feedback for machine translation. Moreover, a study by Kreutzer et al. in 2018 highlights the limitations of relying solely on pairwise preferences for machine translation, suggesting that alternative forms of human feedback might be more effective. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning experts are working on a solution to fine-tune language models for machine translation tasks. They’re facing challenges because people who annotate text can’t easily give them the information they need. Research from 2018 shows that getting ratings or other types of feedback is better than asking people to compare two texts. |
Keywords
» Artificial intelligence » Fine tuning » Machine learning » Optimization » Translation
