Summary of From One to Many: Expanding the Scope Of Toxicity Mitigation in Language Models, by Luiza Pozzobon et al.
From One to Many: Expanding the Scope of Toxicity Mitigation in Language Models
by Luiza Pozzobon, Patrick Lewis, Sara Hooker, Beyza Ermis
First submitted to arxiv on: 6 Mar 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 This paper tackles the challenge of toxicity mitigation in language models as they transition from single-language to multilingual capabilities. The authors recognize a research gap and propose an approach that expands conventional toxicity mitigation techniques to address complexities presented by multiple languages. They employ translated data to evaluate and enhance their mitigation methods, comparing finetuning approaches with retrieval-augmented techniques under static and continual scenarios. This study covers nine languages across various linguistic families and resource levels, providing insights into the complexities of multilingual toxicity mitigation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is important because language models are getting better at understanding many languages, but they’re not good at avoiding toxic words or messages. The authors want to fix this by making their model more aware of different languages and cultures. They do this by using translated data to train the model, so it can learn what’s toxic in each language. They also compare different ways of training the model, like fine-tuning it for specific languages or using big databases of text. The study looks at nine languages that are very different from each other, and it shows how well their approach works. |
Keywords
» Artificial intelligence » Fine tuning
