Summary of Fm2ds: Few-shot Multimodal Multihop Data Synthesis with Knowledge Distillation For Question Answering, by Amirhossein Abaskohi et al.
FM2DS: Few-Shot Multimodal Multihop Data Synthesis with Knowledge Distillation for Question Answering
by Amirhossein Abaskohi, Spandana Gella, Giuseppe Carenini, Issam H. Laradji
First submitted to arxiv on: 9 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); 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 The proposed methodology introduces the first framework for creating a high-quality dataset that enables training models for multimodal multihop question answering, a complex task requiring reasoning over multiple sources of information. The current methods focus on single-hop question answering or a single modality, making them unsuitable for real-world scenarios such as analyzing educational materials or summarizing academic articles. To address this gap, the authors propose a novel 5-stage pipeline that involves acquiring multimodal documents from Wikipedia, synthetically generating high-level questions and answers, and validating them through rigorous criteria to ensure quality data. The methodology is evaluated by training models on the synthesized dataset and testing on two benchmarks, with results demonstrating that models trained on the synthesized data outperform those trained on human-collected data by 1.9 in exact match (EM) on average. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to train computers to answer questions using multiple sources of information, like images and text. Currently, most question-answering systems only work with one type of information or can only answer simple questions. The authors created a special dataset that allows computers to learn how to answer more complex questions by combining different types of information. They tested their approach on two benchmarks and found that the models trained on this new dataset performed better than those trained on human-collected data. |
Keywords
» Artificial intelligence » Question answering
