Summary of Direct Preference Optimization with An Offset, by Afra Amini et al.
Direct Preference Optimization with an Offset
by Afra Amini, Tim Vieira, Ryan Cotterell
First submitted to arxiv on: 16 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 proposed Direct Preference Optimization (DPO) fine-tuning strategy for large language models aligns with human preferences without requiring a reward model or reinforcement learning. Initially developed for binary preference data, DPO optimizes a language model to prefer one response over another. However, not all preference pairs are created equal; some responses may only marginally better than others, while others have stronger preferences. For instance, annotators strongly disprefer toxic content. This paper generalizes DPO with an offset (ODPO), which differentially treats preference pairs based on the magnitude of the preferred response’s likelihood over the dispreferred one. The offset value is determined by the degree of preference. Experimental results demonstrate that ODPO surpasses DPO in aligning language models, particularly when few preference pairs are available. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper improves a way to make large language models like humans want them to be. It’s called Direct Preference Optimization (DPO). Right now, DPO is good at fine-tuning these models using simple “like” or “dislike” preferences. But what if some preferences are stronger than others? For example, annotators really dislike toxic content! The authors propose a new way to make DPO better by considering how strong the preference is. They call it ODPO (DPO with an offset). It works by making the model prefer one response more strongly over another only if there’s a significant difference. This makes ODPO work better, especially when there are few examples to learn from. |
Keywords
* Artificial intelligence * Fine tuning * Language model * Likelihood * Optimization * Reinforcement learning
