Summary of Bootstrapping Language Models with Dpo Implicit Rewards, by Changyu Chen et al.
Bootstrapping Language Models with DPO Implicit Rewards
by Changyu Chen, Zichen Liu, Chao Du, Tianyu Pang, Qian Liu, Arunesh Sinha, Pradeep Varakantham, Min Lin
First submitted to arxiv on: 14 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 paper introduces a novel approach to human alignment in large language models (LLMs), building upon the direct preference optimization (DPO) framework. By utilizing the implicit reward model generated by DPO, the authors propose a self-alignment mechanism that bootstraps the process of aligning LLMs with human preferences. The method, dubbed Self-Alignment with DPO Implicit Cit rEwards (DICE), incorporates refinements such as length-regularized reward shaping and experience replay to enhance the quality of the preference dataset. Experimental results demonstrate significant improvements in alignment, achieving an increase of over 8% in length-controlled win rate on AlpacaEval 2 for various base models. The proposed approach does not rely on external feedback, making it a promising solution for LLM alignment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper shows how to make large language models work better with human ideas. It uses a new method called DICE that takes the rewards from a current model and uses them to create a new preference dataset. This dataset is then used to train the model again, making it more aligned with human preferences. The approach has two special features: one helps remove bias in the data, and the other helps make the training process more efficient. When tested on different models, DICE achieved significant improvements in alignment without needing external feedback. |
Keywords
* Artificial intelligence * Alignment * Optimization
