Chart Question Answering

Chart Question Answering (CQA) focuses on automatically answering natural language questions about data visualizations, such as bar charts and line graphs. Current research emphasizes developing models that effectively combine visual and textual information, often employing multimodal architectures like vision transformers and large language models (LLMs) with techniques such as graph-of-thought reasoning and programmatic solution generation to handle complex logical and numerical reasoning within the chart context. This field is significant because it facilitates automated data analysis and insight extraction from visual data, impacting fields like business intelligence, scientific research, and data reporting.