Summary of Preference Tuning For Toxicity Mitigation Generalizes Across Languages, by Xiaochen Li et al.
Preference Tuning For Toxicity Mitigation Generalizes Across Languages
by Xiaochen Li, Zheng-Xin Yong, Stephen H. Bach
First submitted to arxiv on: 23 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); 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 research explores ways to “detoxify” multilingual Large Language Models (LLMs) by reducing their ability to generate toxic content. The authors investigate a technique called Direct Preference Optimization (DPO), which trains models on English data only, and find that it can effectively reduce toxicity in open-ended text generations across 17 languages, including those like BLOOM, Llama3, and Aya-23. The study also identifies the dual multilinguality property of MLP layers as a key factor explaining DPO’s cross-lingual generalization. The authors use mechanistic interpretability tools to analyze the models’ behavior and show that bilingual sentence retrieval can predict the transferability of DPO preference tuning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you want to make sure language models don’t write mean or hurtful things. This research finds a way to “clean up” these models so they only generate nice, respectful text. The scientists tested their method on many languages and found that it worked well across different types of texts. They even figured out why this method works and how it can be used in other situations. Overall, this study helps make sure language models are safe and respectful for everyone. |
Keywords
» Artificial intelligence » Generalization » Optimization » Transferability
