Summary of Lionguard: Building a Contextualized Moderation Classifier to Tackle Localized Unsafe Content, by Jessica Foo and Shaun Khoo
LionGuard: Building a Contextualized Moderation Classifier to Tackle Localized Unsafe Content
by Jessica Foo, Shaun Khoo
First submitted to arxiv on: 24 Jun 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 proposes LionGuard, a moderation classifier designed specifically for the Singaporean context. The model is trained on Singlish data and outperforms existing APIs by 14% (binary) and up to 51% (multi-label). The authors highlight the importance of localization in safety-tuning large language models (LLMs), which are increasingly used in various applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to make sure large language models don’t produce harmful or offensive content. Right now, most attempts to do this have a Western perspective on what’s safe and what’s not. The researchers created LionGuard, a special kind of classifier that can help keep LLMs safe in the Singapore context. They tested it with Singlish data and found it was way better than existing solutions. This shows that making models work for specific cultures or languages is important. |
