Summary of Synthetic Multimodal Question Generation, by Ian Wu et al.
Synthetic Multimodal Question Generation
by Ian Wu, Sravan Jayanthi, Vijay Viswanathan, Simon Rosenberg, Sina Pakazad, Tongshuang Wu, Graham Neubig
First submitted to arxiv on: 2 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 paper proposes Multimodal Retrieval Augmented Generation (MMRAG), a powerful approach for question-answering over multimodal documents. To evaluate MMRAG, the authors recognize the scarcity of high-quality datasets matching desired question styles and modalities. In response, they introduce SMMQG, a synthetic data generation framework that leverages interplay between a retriever, large language model (LLM), and large multimodal model (LMM) to generate question-answer pairs directly from multimodal documents, conforming questions to specified styles and modalities. The authors use SMMQG to create an MMRAG dataset of 1024 questions over Wikipedia documents and evaluate state-of-the-art models using it, revealing insights into model performance attainable only through style- and modality-specific evaluation data. SMMQG-generated synthetic data quality is comparable to the crowdsourced benchmark MMQA, with downstream evaluation results strongly concuring between both datasets. The authors measure SMMQG data quality via a human study, indicating its potential for real-world applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a way to generate questions and answers from documents that have text, images, audio, or video. This is important because it helps us evaluate how well computer programs can answer questions about these kinds of documents. The authors make a special dataset using this method and test some popular AI models on it. They find that the generated data works just as well as real data for testing these models. |
Keywords
» Artificial intelligence » Large language model » Question answering » Retrieval augmented generation » Synthetic data
