Summary of Spiqa: a Dataset For Multimodal Question Answering on Scientific Papers, by Shraman Pramanick et al.
SPIQA: A Dataset for Multimodal Question Answering on Scientific Papers
by Shraman Pramanick, Rama Chellappa, Subhashini Venugopalan
First submitted to arxiv on: 12 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 SPIQA (Scientific Paper Image Question Answering) dataset is a novel large-scale question-answering dataset designed to interpret complex figures and tables within scientific research articles across various computer science domains. The dataset leverages the capabilities of multimodal large language models (MLLMs) to understand figures, featuring 270K questions divided into training, validation, and three evaluation splits. The authors evaluate current MLLMs using an information-seeking task on interleaved images and text, involving multiple image types such as plots, charts, tables, schematic diagrams, and result visualizations. Additionally, the paper proposes a Chain-of-Thought (CoT) evaluation strategy with in-context retrieval to assess model performance at a fine-grained level. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The SPIQA dataset is a new tool that helps people quickly find answers when reading scientific papers. Right now, there are only a few datasets like this that focus on the images and charts found in these papers. The authors created a big dataset with 270,000 questions to test how well computers can understand these complex figures. They want to see if computers can learn from looking at the text and images together. |
Keywords
» Artificial intelligence » Question answering
