Papers with MMRAG
Synthetic Multimodal Question Generation (2024.findings-emnlp)
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Ian Wu, Sravan Jayanthi, Vijay Viswanathan, Simon Rosenberg, Sina Pakazad, Tongshuang Wu, Graham Neubig
| Challenge: | Multimodal Retrieval Augmented Generation (MMRAG) is a powerful approach to question-answering over multimodal documents. |
| Approach: | They propose a synthetic data generation framework that leverages interplay between a retriever, large language model and large multimodal model to generate question and answer pairs directly from multimodal documents. |
| Outcome: | The proposed framework generates question and answer pairs from 1024 questions over Wikipedia documents and evaluates state-of-the-art models using it. |
CoRe-MMRAG: Cross-Source Knowledge Reconciliation for Multimodal RAG (2025.acl-long)
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| Challenge: | Recent advances in Multimodal Large Language Models have significantly improved reasoning and generation tasks by leveraging joint vision-language representations. |
| Approach: | They propose a framework that reconciles inconsistencies across knowledge sources . they use a four-stage pipeline to generate an internal response from parametric knowledge . |
| Outcome: | Experiments on KB-VQA show that CoRe-MMRAG achieves performance gains of 5.6% and 9.3% over baseline methods. |