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ChartInstruct: Instruction Tuning for Chart Comprehension and Reasoning
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Charts provide visual representations of data and are widely used for analyzing information, addressing queries, and conveying insights to others. Various chart-related downstream tasks have emerged recently, such as question-answering and summarization. A common strategy to solve these tasks is to fine-tune various models originally trained on vision tasks language. However, such task-specific models are not capable of solving a wide range of chart-related tasks, constraining their real-world applicability. To overcome these challenges, we introduce ChartInstruct: a novel chart-specific vision-language Instruction-following dataset comprising 191K instructions generated with 71K charts. We then present two distinct systems for instruction tuning on such datasets: (1) an end-to-end model that connects a vision encoder for chart understanding with a LLM; and (2) a pipeline model that employs a two-step approach to extract chart data tables and input them into the LLM. In experiments on four downstream tasks, we first show the effectiveness of our model--achieving a new set of state-of-the-art results. Further evaluation shows that our instruction-tuning approach supports a wide array of real-world chart comprehension and reasoning scenarios, thereby expanding the scope and applicability of our models to new kinds of tasks.
Forward citations
Cited by 5 Pith papers
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ChartMind is a new bilingual chart QA benchmark, and ChartLLM's structured context extraction yields higher scores than three existing prompting paradigms in the paper's evaluations.
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CHAOS: Chart Analysis with Outlier Samples
A chart perturbation robustness benchmark with five textual and ten visual distortion types, three human-calibrated severity levels, and evaluations of 13 MLLMs on ChartQA and chart summarization.
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Adaptive Sparse Softmax: An Effective and Efficient Softmax Variant
The preprint's abstract claims a sparse softmax variant that masks non-competitive classes and accelerates training, but the provided body contains an unrelated chart-captioning paper and none of the claimed method.
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ChartSketcher: Reasoning with Multimodal Feedback and Reflection for Chart Understanding
ChartSketcher has a multimodal LLM sketch intermediate reasoning steps directly on chart images and feed those sketches back as visual feedback, improving chart QA accuracy over its base model.
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