SciChart: Visual Question Answering and Reasoning for Scientific Spectral Chart
TL;DR
This work designs two tasks, basic question answering (BasicQA) and reasoning-based question answering (ReaQA), to evaluate the models' ability to directly extract information from charts, and understand the textual and visual information for reasoning.
Abstract
Charts play a key role in scientific research, offering a concise and visual way to present complex data. For Multimodal Large Language Models (MLLMs), the ability to comprehend charts is critical, as it requires both visual perception and reasoning that bridges graphical and textual information. However, existing chart question answering (QA) datasets are monolingual with simple questions, making current evaluation benchmarks inadequate for the rapid advancements in MLLM performance. Therefore, we propose a multilingual scientific spectral Chart QA dataset, termed SciChart. We design two tasks, basic question answering (BasicQA) and reasoning-based question answering (ReaQA), to evaluate the models' ability to 1) directly extract information from charts, and 2) understand the textual and visual information for reasoning. We build 1,100 ReaQA and over 10,000 BasicQA samples. All samples are manually curated and annotated by human experts. We also conduct extensive experiments with state-of-the-art models to establish SciChart benchmarks. Experimental results show a huge gap between the performance of existing models (Claude-3.7 45.12%) and humans (83.84%).