Summary of Sage-rt: Synthetic Alignment Data Generation For Safety Evaluation and Red Teaming, by Anurakt Kumar et al.
SAGE-RT: Synthetic Alignment data Generation for Safety Evaluation and Red Teaming
by Anurakt Kumar, Divyanshu Kumar, Jatan Loya, Nitin Aravind Birur, Tanay Baswa, Sahil Agarwal, Prashanth Harshangi
First submitted to arxiv on: 14 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Cryptography and Security (cs.CR)
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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 pipeline called SAGE (Synthetic Alignment data Generation for Safety Evaluation and Red Teaming) is introduced, which generates synthetic alignment and red-teaming data for large language models. The existing methods are limited in creating nuanced and diverse datasets, providing control over the generation process, or requiring manual seed data. SAGE addresses these limitations by using a detailed taxonomy to produce safety-alignment and red-teaming data across various topics. The pipeline generated 51,000 prompt-response pairs, covering over 1,500 topics of harmfulness and variations in jailbreaking prompts faced by large language models. The results show that the red-teaming data generated through SAGE successfully jailbreaks state-of-the-art LLMs in multiple sub-categories and leaf-categories. The approach avoids mode collapse and lack of nuance in the generation pipeline by ensuring detailed coverage of harmful topics using iterative topic expansion and conditioning outputs on raw-text. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SAGE is a new way to make large language models safer. It creates fake data that tests the limits of these AI systems, helping them learn to behave better. The old methods didn’t do this well enough, so SAGE uses a special system to create lots of different prompts and responses about harmful topics. This helps the AI learn to avoid mistakes and behave safely. SAGE even outsmarts top-performing AI models by creating fake data that can “jailbreak” them. This is important because it keeps our AI systems from getting too powerful and causing harm. |
Keywords
» Artificial intelligence » Alignment » Prompt
