Papers with chart-specific vision-language Instruction-following dataset

1 papers
ChartInstruct: Instruction Tuning for Chart Comprehension and Reasoning (2024.findings-acl)

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Challenge: Charts provide visual representations of data and are used for analyzing information, addressing queries, and conveying insights to others.
Approach: They propose a chart-specific vision-language Instruction-following dataset with 191K instructions and a pipeline model that extracts chart data tables and inputs them into a LLM.
Outcome: The proposed model can solve a wide range of chart-related tasks, achieving state-of-the-art results on four tasks.

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