Summary of Mdpo: Conditional Preference Optimization For Multimodal Large Language Models, by Fei Wang et al.
mDPO: Conditional Preference Optimization for Multimodal Large Language Models
by Fei Wang, Wenxuan Zhou, James Y. Huang, Nan Xu, Sheng Zhang, Hoifung Poon, Muhao Chen
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); 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 proposes a new method for aligning large language models (LLMs) with human preferences in multimodal scenarios. The proposed approach, called mDPO, addresses an issue known as the unconditional preference problem, where LLMs prioritize language-based preferences over image-based ones. To achieve this, mDPO optimizes both language and image preferences simultaneously, while also introducing a reward anchor to ensure positive rewards for chosen responses. Experimental results on two multimodal LLMs and three benchmarks show that mDPO improves model performance and reduces hallucination. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study helps large language models understand what people like about images. Right now, these models often prioritize text-based preferences over image-based ones. To fix this, the researchers created a new approach called mDPO. It makes sure both language and image preferences are taken into account when deciding what’s good or bad. This means the models will be better at understanding what people like about images. The results show that this method works well for reducing mistakes in what the model thinks is good. |
Keywords
» Artificial intelligence » Hallucination
