Summary of Mppo: Multi Pair-wise Preference Optimization For Llms with Arbitrary Negative Samples, by Shuo Xie et al.
MPPO: Multi Pair-wise Preference Optimization for LLMs with Arbitrary Negative Samples
by Shuo Xie, Fangzhi Zhu, Jiahui Wang, Lulu Wen, Wei Dai, Xiaowei Chen, Junxiong Zhu, Kai Zhou, Bo Zheng
First submitted to arxiv on: 13 Dec 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 This research paper introduces a novel algorithm called MPPO (Maximum Preference-based Policy Optimization) to optimize Large Language Models (LLMs) using human feedback. Unlike existing methods like DPO and KTO, which rely on Reinforcement Learning from Human Feedback (RLHF) and require a reference model, MPPO leverages the average likelihood of model responses to fit the reward function, maximizing preference data utilization. The study compares Point-wise, Pair-wise, and List-wise implementations, finding that the Pair-wise approach achieves the best performance in enhancing model response quality. Experimental results demonstrate MPPO’s outstanding performance across various benchmarks, including MT-Bench and Arena-Hard, outperforming existing algorithms like DPO, ORPO, and SimPO. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making language models better by using human feedback. Right now, some methods are limited because they need a lot of data and memory. The researchers developed a new way to optimize these models called MPPO. They tested it on different scenarios and found that it works really well. In fact, MPPO outperformed other existing methods in many cases. This is important because it means we can make language models even more accurate and helpful. |
Keywords
» Artificial intelligence » Likelihood » Optimization » Reinforcement learning from human feedback » Rlhf
