Summary of Multi-reference Preference Optimization For Large Language Models, by Hung Le et al.
Multi-Reference Preference Optimization for Large Language Models
by Hung Le, Quan Tran, Dung Nguyen, Kien Do, Saloni Mittal, Kelechi Ogueji, Svetha Venkatesh
First submitted to arxiv on: 26 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this research paper, the authors aim to improve the alignment of Large Language Models (LLMs) with human intentions and values. They propose a novel approach called Multi-Reference Preference Optimization (MRPO), which leverages the collective power of multiple pretrained LLMs to enhance preference learning capabilities. This method builds upon recent advances in direct preference optimization (DPO) and addresses the limitation of single-reference models. The authors demonstrate that MRPO finetunes LLMs to generalize better across various preference data, regardless of data scarcity or abundance, and improves performance in downstream natural language processing tasks such as GSM8K and TruthfulQA. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are incredibly powerful tools that can be used for many different purposes. However, they need to be “aligned” with what humans want them to do, otherwise they might not always make the right choices. One way to align LLMs is by fine-tuning them using data about what humans prefer. This paper introduces a new method called MRPO (Multi-Reference Preference Optimization) that allows us to use multiple reference models instead of just one. This makes it more powerful and able to learn from a wider range of sources. |
Keywords
» Artificial intelligence » Alignment » Fine tuning » Natural language processing » Optimization