ChartInstruct: Instruction Tuning for Chart Comprehension and Reasoning
Ahmed Masry, Mehrad Shahmohammadi, Md Rizwan Parvez, Enamul Hoque, Shafiq Joty · 2024
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 questionanswering 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 chartrelated 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. * Equal contribution.