Summary of Class-rag: Real-time Content Moderation with Retrieval Augmented Generation, by Jianfa Chen et al.
Class-RAG: Real-Time Content Moderation with Retrieval Augmented Generation
by Jianfa Chen, Emily Shen, Trupti Bavalatti, Xiaowen Lin, Yongkai Wang, Shuming Hu, Harihar Subramanyam, Ksheeraj Sai Vepuri, Ming Jiang, Ji Qi, Li Chen, Nan Jiang, Ankit Jain
First submitted to arxiv on: 18 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 A novel approach to robust content moderation for Generative AI systems is proposed in this paper. The challenge lies in distinguishing between safe and unsafe inputs, often with subtle differences. Existing classifiers, including those used by humans, struggle without context or explanation. To address this issue, the authors introduce a Classification approach employing Retrieval-Augmented Generation (Class-RAG). Class-RAG extends the capabilities of its base LLM through access to a retrieval library that can be dynamically updated for immediate risk mitigation. Compared to model fine-tuning, Class-RAG demonstrates flexibility and transparency in decision-making, outperforming on classification tasks while being more robust against adversarial attacks. Empirical studies support these claims. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers developed a new way to keep Generative AI systems safe from harmful content. This is important because even small differences can make it hard for computers or humans to decide what’s okay and what’s not. They created a system called Class-RAG that uses a special library to help classify content quickly and accurately. This approach is better than just updating the computer’s model, which takes time and money. The results show that Class-RAG works well and can even improve with more data. This means that developers can respond faster to new problems and keep their systems safe. |
Keywords
» Artificial intelligence » Classification » Fine tuning » Rag » Retrieval augmented generation
