Classifier-free guidance in LLMs Safety
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916515079520256 |
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| author | Smirnov, Roman |
| author_facet | Smirnov, Roman |
| contents | The paper describes LLM unlearning without a retaining dataset, using the ORPO reinforcement learning method with inference enhanced by modified classifier-free guidance. Significant improvement in unlearning, without degradation of the model, is achieved through direct training on synthetic replacement data in CFG-aware training regime, with classifier-free guidance applied during the inference. This article is an extended version of the NeurIPS 2024 LLM-PC submission, which was awarded second prize. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_06846 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Classifier-free guidance in LLMs Safety Smirnov, Roman Machine Learning Artificial Intelligence The paper describes LLM unlearning without a retaining dataset, using the ORPO reinforcement learning method with inference enhanced by modified classifier-free guidance. Significant improvement in unlearning, without degradation of the model, is achieved through direct training on synthetic replacement data in CFG-aware training regime, with classifier-free guidance applied during the inference. This article is an extended version of the NeurIPS 2024 LLM-PC submission, which was awarded second prize. |
| title | Classifier-free guidance in LLMs Safety |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2412.06846 |