Summary of Exploring Straightforward Conversational Red-teaming, by George Kour and Naama Zwerdling and Marcel Zalmanovici and Ateret Anaby-tavor and Ora Nova Fandina and Eitan Farchi
Exploring Straightforward Conversational Red-Teaming
by George Kour, Naama Zwerdling, Marcel Zalmanovici, Ateret Anaby-Tavor, Ora Nova Fandina, Eitan Farchi
First submitted to arxiv on: 7 Sep 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 This research paper explores the potential risks of large language models (LLMs) in business dialogue systems by examining the use of off-the-shelf LLMs for “red-teaming” purposes. Red-teaming involves an attacker model attempting to elicit undesired responses from a target model through multi-turn conversations. The study compares single-turn and conversational red-teaming tactics, revealing that off-the-shelf models can be effective in this role, even adjusting their attack strategies based on past attempts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are used in business dialogue systems but pose security and ethical risks. This paper looks at using these models for “red-teaming” – making them behave badly to test the target model’s defenses. They try different ways of doing this, with or without conversations between turns. The results show that off-the-shelf models can be good at this job and even change their approach based on how they did before. |
