Doc2Chart: Intent-Driven Zero-Shot Chart Generation from Documents

• URL: http://arxiv.org/abs/2507.14819v1
• Date: Sun, 20 Jul 2025 04:34:59 GMT
• Title: Doc2Chart: Intent-Driven Zero-Shot Chart Generation from Documents
• Authors: Akriti Jain, Pritika Ramu, Aparna Garimella, Apoorv Saxena,
• Abstract summary: We introduce the task of intent-based chart generation from documents.<n>The goal is to generate a chart adhering to the intent and grounded on the document(s) in a zero-shot setting.<n>We propose an attribution-based metric that uses a structured textual representation of charts.
• Score: 7.616682226138909
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) have demonstrated strong capabilities in transforming text descriptions or tables to data visualizations via instruction-tuning methods. However, it is not straightforward to apply these methods directly for a more real-world use case of visualizing data from long documents based on user-given intents, as opposed to the user pre-selecting the relevant content manually. We introduce the task of intent-based chart generation from documents: given a user-specified intent and document(s), the goal is to generate a chart adhering to the intent and grounded on the document(s) in a zero-shot setting. We propose an unsupervised, two-staged framework in which an LLM first extracts relevant information from the document(s) by decomposing the intent and iteratively validates and refines this data. Next, a heuristic-guided module selects an appropriate chart type before final code generation. To assess the data accuracy of the generated charts, we propose an attribution-based metric that uses a structured textual representation of charts, instead of relying on visual decoding metrics that often fail to capture the chart data effectively. To validate our approach, we curate a dataset comprising of 1,242 $<$intent, document, charts$>$ tuples from two domains, finance and scientific, in contrast to the existing datasets that are largely limited to parallel text descriptions/ tables and their corresponding charts. We compare our approach with baselines using single-shot chart generation using LLMs and query-based retrieval methods; our method outperforms by upto $9$ points and $17$ points in terms of chart data accuracy and chart type respectively over the best baselines.

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