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In this work, we present SciGraphQA, a synthetic multi-turn question-answerdataset related to academic graphs. SciGraphQA is 13 times larger thanChartVQA, the previously largest chart-visual question-answering dataset. It isalso the largest open-sourced chart VQA dataset with non-synthetic charts. Tobuild our dataset, we selected 290,000 Computer Science or Machine LearningArXiv papers published between 2010 and 2020, and then used Palm-2 to generate295K samples of open-vocabulary multi-turn question-answering dialogues aboutthe graphs. As context, we provided the text-only Palm-2 with paper title,abstract, paragraph mentioning the graph, and rich text contextual data fromthe graph itself, obtaining dialogues with an average 2.23 question-answerturns for each graph. We asked GPT-4 to assess the matching quality of ourquestion-answer turns given the paper's context, obtaining an average rating of8.7/10 on our 3K test set. We evaluated the 0-shot capability of the mostpopular MLLM models such as LLaVa, mPLUGowl, BLIP-2, and openFlamingo's on ourdataset, finding LLaVA-13B being the most performant with a CIDEr score of0.08. We further enriched the question prompts for LLAVA by including theserialized data tables extracted from the graphs using the DePlot model,boosting LLaVA's 0-shot CIDEr to 0.15. To verify the validity of our dataset,we also fine-tuned LLaVa using our dataset, reaching a substantially higherCIDEr score of 0.26. We anticipate further accuracy improvement by includingsegmentation mask tokens and leveraging larger LLM backbones coupled withemergent prompting techniques. Our code and data are open-sourced.

SciGraphQA: A Large-Scale Synthetic Multi-Turn Question-Answering Dataset for Scientific Graphs