Summary of Direct Alignment Of Language Models Via Quality-aware Self-refinement, by Runsheng Yu et al.
Direct Alignment of Language Models via Quality-Aware Self-Refinement
by Runsheng Yu, Yong Wang, Xiaoqi Jiao, Youzhi Zhang, James T. Kwok
First submitted to arxiv on: 31 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposes a novel approach to Direct Policy Optimization (DPO) for aligning Large Language Models (LLMs) with human preferences. Unlike traditional RLHF, DPO eliminates the need for an LLM-based reward model, reducing training time. However, this method neglects the relative qualities of positive and negative responses, leading to sub-optimal outcomes. To address this limitation, the authors introduce a refinement function that leverages intrinsic knowledge within the on-the-fly fine-tuned LLM. This function estimates the quality of both positive and negative responses, enabling self-refinement of the loss function. The proposed approach is integrated into DPO and its variant IPO, demonstrating improved performance across various evaluators. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper finds a way to make Large Language Models (LLMs) behave better by using human feedback. Currently, there are two ways to do this: RLHF or DPO. DPO is faster because it doesn’t need an extra reward model, but it can lead to problems if it’s not done correctly. To fix this, the authors came up with a new way to make LLMs better by using their own knowledge. This helps refine how well the LLM does and makes it work better overall. |
Keywords
» Artificial intelligence » Loss function » Optimization » Rlhf
