Summary of Progressive Multimodal Reasoning Via Active Retrieval, by Guanting Dong et al.
Progressive Multimodal Reasoning via Active Retrieval
by Guanting Dong, Chenghao Zhang, Mengjie Deng, Yutao Zhu, Zhicheng Dou, Ji-Rong Wen
First submitted to arxiv on: 19 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Information Retrieval (cs.IR)
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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 proposed AR-MCTS framework enhances the performance of multimodal large language models (MLLMs) on multi-step multimodal reasoning tasks by leveraging Active Retrieval and Monte Carlo Tree Search. The unified retrieval module retrieves key insights from a hybrid-modal corpus, while MCTS with active retrieval generates step-wise annotations to improve diversity and reliability. A process reward model aligns progressively to support automatic verification of multimodal reasoning tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a new approach for improving the performance of multimodal language models on complex reasoning tasks. By using Active Retrieval and Monte Carlo Tree Search, the AR-MCTS framework can retrieve key insights and generate step-wise annotations to improve diversity and reliability. The results show that this framework is effective in enhancing the performance of various multimodal models. |
