Summary of Mllm Is a Strong Reranker: Advancing Multimodal Retrieval-augmented Generation Via Knowledge-enhanced Reranking and Noise-injected Training, by Zhanpeng Chen et al.
MLLM Is a Strong Reranker: Advancing Multimodal Retrieval-augmented Generation via Knowledge-enhanced Reranking and Noise-injected Training
by Zhanpeng Chen, Chengjin Xu, Yiyan Qi, Jian Guo
First submitted to arxiv on: 31 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: 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 Multimodal Large Language Models (MLLMs) have shown impressive capabilities in processing and generating content across multiple data modalities. However, their reliance on static training data leads to outdated information and limited contextual awareness, hindering accurate responses in dynamic contexts. Integrating Multimodal Retrieval-augmented Generation (Multimodal RAG) offers a promising solution, but the multi-granularity noisy correspondence (MNC) problem hinders retrieval and generation. To address these limitations, we propose RagVL, a novel framework combining knowledge-enhanced reranking and noise-injected training to overcome outdated information and limited contextual awareness. We instruction-tune the MLLM with a simple yet effective template to induce its ranking ability and serve it as a reranker to filter retrieved images precisely. For generation, we inject visual noise during training at data and token levels to enhance robustness. Our method effectively addresses these limitations, demonstrating improved performance on image-based query answering tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Multimodal Large Language Models can process multiple types of content well. But they have a problem: they only learn from data that was created before, which means their information is outdated and limited. This makes it hard for them to provide accurate answers when the context changes quickly. To solve this, we developed RagVL, a new framework that combines two important ideas: using knowledge to improve retrieval and adding noise to training to make the model more robust. We tested our method on two datasets where MLLMs need to retrieve and reason about images to answer questions. The results show that RagVL is effective in overcoming these limitations. |
Keywords
» Artificial intelligence » Rag » Retrieval augmented generation » Token
