Summary of Atoxia: Red-teaming Large Language Models with Target Toxic Answers, by Yuhao Du et al.
Atoxia: Red-teaming Large Language Models with Target Toxic Answers
by Yuhao Du, Zhuo Li, Pengyu Cheng, Xiang Wan, Anningzhe Gao
First submitted to arxiv on: 27 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 Medium Difficulty summary: This research paper proposes a novel method, called Atoxia, to identify and mitigate the risks of large language models (LLMs) generating harmful content. The authors highlight the vulnerability of LLMs to adversarial prompts that induce them to output negative social impacts. To address this issue, they develop an attacker model within a reinforcement learning framework that generates user queries and misleading answers to examine the internal defects of given LLMs. The proposed method is evaluated on various red-teaming benchmarks, including AdvBench and HH-Harmless, demonstrating its effectiveness in detecting safety risks in open-source and state-of-the-art black-box models like GPT-4o. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This research paper talks about a problem with big language models. These models can sometimes produce harmful content when given the right prompts. The authors want to fix this issue by creating a new way to test these models for their safety risks. They call it Atoxia and use it to generate fake user queries and answers that trick the model into showing its weaknesses. They tested this method on different types of models and found that it works well in detecting potential problems. |
Keywords
» Artificial intelligence » Gpt » Reinforcement learning
