SciGraphQA is a large-scale, open-domain dataset focused on generating multi-turn conversational question-answering dialogues centered around understanding and describing scientific graphs and figures. Each sample in ScFiGraphQA consists of a scientific graph image sourced from papers on ArXiv, accompanied by rich textual context including the paper's title, abstract, figure caption, and a paragraph The key motivation behind SciGraphQA is providing a large-scale resource to support research and development of multi-modal AI systems that can engage in informative, open-ended conversations about graphs Potential use cases of SciGraphQA include pre-training and benchmarking multi-modal conversational models for scientific graph comprehension, building AI assistants that can discuss data insights, and The academic source material also provides a way to evaluate model capabilities on expert-level graphs spanning diverse topics and complex visual encodings.
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…The images are synthetic, scientific-style figures from five classes: line plots, dot-line plots, vertical and horizontal bar graphs, and pie charts.
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…Each training and validation image is also associated with scene graph annotations describing the classes and attributes of those objects in the scene, and their pairwise relations.
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…CLEVR dataset consists of: a training set of 70k images and 700k questions, a validation set of 15k images and 150k questions, a test set of 15k images and 150k questions about objects, answers, scene graphs
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…We create the puzzles to encompass a diverse array of mathematical and algorithmic topics such as boolean logic, combinatorics, graph theory, optimization, search, etc., aiming to evaluate the gap between
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