LLM-Assisted Red Teaming of Diffusion Models through "Failures Are Fated, But Can Be Faded"
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866916448766525440 |
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| author | Sagar, Som Taparia, Aditya Senanayake, Ransalu |
| author_facet | Sagar, Som Taparia, Aditya Senanayake, Ransalu |
| contents | In large deep neural networks that seem to perform surprisingly well on many tasks, we also observe a few failures related to accuracy, social biases, and alignment with human values, among others. Therefore, before deploying these models, it is crucial to characterize this failure landscape for engineers to debug or audit models. Nevertheless, it is infeasible to exhaustively test for all possible combinations of factors that could lead to a model's failure. In this paper, we improve the "Failures are fated, but can be faded" framework (arXiv:2406.07145)--a post-hoc method to explore and construct the failure landscape in pre-trained generative models--with a variety of deep reinforcement learning algorithms, screening tests, and LLM-based rewards and state generation. With the aid of limited human feedback, we then demonstrate how to restructure the failure landscape to be more desirable by moving away from the discovered failure modes. We empirically demonstrate the effectiveness of the proposed method on diffusion models. We also highlight the strengths and weaknesses of each algorithm in identifying failure modes. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2410_16738 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LLM-Assisted Red Teaming of Diffusion Models through "Failures Are Fated, But Can Be Faded" Sagar, Som Taparia, Aditya Senanayake, Ransalu Machine Learning In large deep neural networks that seem to perform surprisingly well on many tasks, we also observe a few failures related to accuracy, social biases, and alignment with human values, among others. Therefore, before deploying these models, it is crucial to characterize this failure landscape for engineers to debug or audit models. Nevertheless, it is infeasible to exhaustively test for all possible combinations of factors that could lead to a model's failure. In this paper, we improve the "Failures are fated, but can be faded" framework (arXiv:2406.07145)--a post-hoc method to explore and construct the failure landscape in pre-trained generative models--with a variety of deep reinforcement learning algorithms, screening tests, and LLM-based rewards and state generation. With the aid of limited human feedback, we then demonstrate how to restructure the failure landscape to be more desirable by moving away from the discovered failure modes. We empirically demonstrate the effectiveness of the proposed method on diffusion models. We also highlight the strengths and weaknesses of each algorithm in identifying failure modes. |
| title | LLM-Assisted Red Teaming of Diffusion Models through "Failures Are Fated, But Can Be Faded" |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2410.16738 |