Summary of Enhancing Llm Safety Via Constrained Direct Preference Optimization, by Zixuan Liu et al.
Enhancing LLM Safety via Constrained Direct Preference Optimization
by Zixuan Liu, Xiaolin Sun, Zizhan Zheng
First submitted to arxiv on: 4 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 The rapidly increasing capabilities of large language models (LLMs) require aligning AI systems with diverse human preferences, balancing usefulness and safety. A constrained Reinforcement Learning from Human Feedback (RLHF) framework is explored to achieve this alignment. However, existing approaches are computationally expensive and unstable. This paper introduces Constrained DPO (C-DPO), a novel extension of the Direct Preference Optimization (DPO) approach for fine-tuning LLMs that is efficient and lightweight. C-DPO integrates dual gradient descent and DPO to identify an optimal trade-off between helpfulness and harmlessness without reinforcement learning. The method provides a safety guarantee, achieving higher rewards under the same constraint compared to safe RLHF approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are getting really good at understanding human language! But this raises questions about how we can make sure these AI systems are safe and useful for everyone. One way to do this is by giving them feedback from humans, but this approach has some problems. In this paper, the authors come up with a new idea called Constrained DPO that helps fine-tune large language models to be both helpful and harmless. |
Keywords
* Artificial intelligence * Alignment * Fine tuning * Gradient descent * Optimization * Reinforcement learning * Reinforcement learning from human feedback * Rlhf
