Summary of Curiosity-driven Red-teaming For Large Language Models, by Zhang-wei Hong et al.
Curiosity-driven Red-teaming for Large Language Models
by Zhang-Wei Hong, Idan Shenfeld, Tsun-Hsuan Wang, Yung-Sung Chuang, Aldo Pareja, James Glass, Akash Srivastava, Pulkit Agrawal
First submitted to arxiv on: 29 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 recent study proposes a novel approach to generating test cases for large language models (LLMs) that can elicit undesirable responses, such as toxic or incorrect content. The current method relies on human testers, which is time-consuming and expensive. To overcome this limitation, the authors draw inspiration from curiosity-driven exploration in reinforcement learning (RL) to optimize for novelty. Their method, called Curiosity-Driven Red Teaming (CRT), achieves greater coverage of test cases while maintaining their effectiveness compared to existing methods. CRT successfully provokes toxic responses from the LLaMA2 model, which has been fine-tuned using human preferences to avoid toxic outputs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can generate incorrect or toxic content, but current testing methods rely on expensive and time-consuming human testers. A new approach uses reinforcement learning (RL) to optimize for novelty and test more prompts effectively. This method, called Curiosity-Driven Red Teaming, is better at finding unwanted responses than previous methods. |
Keywords
* Artificial intelligence * Reinforcement learning
