Summary of Lire: Listwise Reward Enhancement For Preference Alignment, by Mingye Zhu et al.
LIRE: listwise reward enhancement for preference alignment
by Mingye Zhu, Yi Liu, Lei Zhang, Junbo Guo, Zhendong Mao
First submitted to arxiv on: 22 May 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 This paper proposes a new approach called Listwise Reward Enhancement for Preference Alignment (LIRE) to align Large Language Models (LLMs) with human values. LIRE leverages Reinforcement Learning from Human Feedback (RLHF) to optimize rewards in a gradient-based framework, allowing for efficient training and scalability. The method eliminates the need for online sampling during training, making it straightforward to implement and requiring minimal parameter tuning. Experimental results show that LIRE outperforms existing methods on dialogue and summarization tasks, with good transferability to out-of-distribution data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Recently, researchers have made significant progress in aligning Large Language Models (LLMs) with human values. This paper introduces a new approach called Listwise Reward Enhancement for Preference Alignment (LIRE). LIRE is designed to make it easier and more efficient to train LLMs that are aligned with human values. |
Keywords
» Artificial intelligence » Alignment » Reinforcement learning from human feedback » Rlhf » Summarization » Transferability
