iART - Imitation guided Automated Red Teaming
Automated Red-teaming;Large Language Models (LLMs);Reinforcement Learning;Imitation
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A new computationally efficient imitation-guided reinforcement learning approach for red teaming (iART) LLMs
@misc{
mousavi2024iart,
title={i{ART} - Imitation guided Automated Red Teaming},
author={Sajad Mousavi and Desik Rengarajan and Ashwin Ramesh Babu and Vineet Gundecha and Ricardo Luna Gutierrez and Antonio Guillen and Avisek Naug and Soumyendu Sarkar},
year={2024},
url={https://openreview.net/forum?id=DcMPfSTLN2}
}
The potential of large language models (LLMs) is substantial, yet they also carry the risk of generating harmful responses. An automatic "red teaming" process constructs test cases designed to elicit unfavorable responses from these models. A successful generator must provoke undesirable responses from the target LLMs with test cases that exemplify diversity. Current methods often struggle to balance quality (i.e., the harmfulness of responses) and diversity (i.e., the range of scenarios) in testing, typically sacrificing one to enhance the other, and relying on non-optimal exhaustive comparison approaches. To address these challenges, we introduce an imitation-guided reinforcement learning approach to learn optimal red teaming strategies that generate both diverse and high-quality test cases without exhaustive searching. Our proposed method, Imitation-guided Automated Red Teaming (iART), is evaluated across various LLMs fine-tuned for different tasks. We demonstrate that iART achieves not only diverse test sets but also elicits undesirable responses from the target LLM in a computationally efficient manner.
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~Sajad_Mousavi1 ~Desik_Rengarajan1 ~Ashwin_Ramesh_Babu1 ~Vineet_Gundecha1 ~Ricardo_Luna_Gutierrez1 ~Antonio_Guillen1 ~Avisek_Naug1 ~Soumyendu_Sarkar1
Sajad Mousavi Desik Rengarajan Ashwin Ramesh Babu Vineet Gundecha Ricardo Luna Gutierrez Antonio Guillen Avisek Naug Soumyendu Sarkar
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Automated Red-teaming Large Language Models (LLMs) Reinforcement Learning Imitation
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mousavi|iart_imitation_guided_automated_red_teaming
foundation or frontier models, including LLMs
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iART - Imitation guided Automated Red Teaming
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