Summary of Toxilab: How Well Do Open-source Llms Generate Synthetic Toxicity Data?, by Zheng Hui et al.
ToxiLab: How Well Do Open-Source LLMs Generate Synthetic Toxicity Data?
by Zheng Hui, Zhaoxiao Guo, Hang Zhao, Juanyong Duan, Lin Ai, Yinheng Li, Julia Hirschberg, Congrui Huang
First submitted to arxiv on: 18 Nov 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 The paper explores the potential of open-source large language models (LLMs) for generating synthetic data to improve hate speech detection. The researchers utilized controlled prompting and supervised fine-tuning techniques to enhance data quality and diversity. They evaluated six open-source LLMs on five datasets, assessing their ability to generate diverse, high-quality harmful data while minimizing hallucination and duplication. The results show that Mistral consistently outperforms other models, and supervised fine-tuning significantly enhances data reliability and diversity. The study also discusses the trade-offs between prompt-based and fine-tuned toxic data synthesis, real-world deployment challenges, and ethical considerations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to make computers better at detecting hate speech on the internet. It uses special machines called language models to create fake text that might contain hateful language. The researchers want to see if these language models can be trained to create lots of different examples of this kind of text, without just copying and pasting the same things over again. They tested six of these language models on five big sets of text, and found that one model called Mistral does a really good job of creating new and diverse examples of hateful text. The study also talks about how to use these language models in real-life situations, and what kind of problems might come up. |
Keywords
» Artificial intelligence » Fine tuning » Hallucination » Prompt » Prompting » Supervised » Synthetic data
