Charts-of-Thought: Enhancing LLM Visualization Literacy Through Structured Data Extraction

TL;DR

This paper introduces the Charts-of-Thought prompting technique, significantly improving the visualization literacy of large language models and surpassing human performance on the VLAT benchmark.

Contribution

The paper presents a novel structured prompting method that enhances LLMs' ability to interpret visualizations, establishing a new benchmark for visualization literacy.

Findings

Claude-3.7-sonnet scored 50.17, exceeding human baseline of 28.82

Performance improved across all models with structured prompting

Claude-3.7 correctly answered 100% of questions for several chart types

Abstract

This paper evaluates the visualization literacy of modern Large Language Models (LLMs) and introduces a novel prompting technique called Charts-of-Thought. We tested three state-of-the-art LLMs (Claude-3.7-sonnet, GPT-4.5 preview, and Gemini-2.0-pro) on the Visualization Literacy Assessment Test (VLAT) using standard prompts and our structured approach. The Charts-of-Thought method guides LLMs through a systematic data extraction, verification, and analysis process before answering visualization questions. Our results show Claude-3.7-sonnet achieved a score of 50.17 using this method, far exceeding the human baseline of 28.82. This approach improved performance across all models, with score increases of 21.8% for GPT-4.5, 9.4% for Gemini-2.0, and 13.5% for Claude-3.7 compared to standard prompting. The performance gains were consistent across original and modified VLAT charts, with Claude correctly answering 100% of questions for several chart types that previously challenged LLMs. Our study reveals that modern multimodal LLMs can surpass human performance on visualization literacy tasks when given the proper analytical framework. These findings establish a new benchmark for LLM visualization literacy and demonstrate the importance of structured prompting strategies for complex visual interpretation tasks. Beyond improving LLM visualization literacy, Charts-of-Thought could also enhance the accessibility of visualizations, potentially benefiting individuals with visual impairments or lower visualization literacy.