CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal LLMs
This work addresses the need for realistic benchmarks in chart understanding for researchers and developers applying MLLMs to real-world tasks like scientific or financial analysis, though it is incremental as it focuses on evaluation rather than new methods.
The paper tackles the problem of overestimating chart understanding capabilities in multimodal LLMs by introducing CharXiv, a comprehensive evaluation suite with 2,323 natural charts from arXiv papers, revealing that proprietary models like GPT-4o achieve 47.1% accuracy while open-source models like InternVL Chat V1.5 achieve 29.2%, far below human performance of 80.5%.
Chart understanding plays a pivotal role when applying Multimodal Large Language Models (MLLMs) to real-world tasks such as analyzing scientific papers or financial reports. However, existing datasets often focus on oversimplified and homogeneous charts with template-based questions, leading to an over-optimistic measure of progress. We demonstrate that although open-source models can appear to outperform strong proprietary models on these benchmarks, a simple stress test with slightly different charts or questions can deteriorate performance by up to 34.5%. In this work, we propose CharXiv, a comprehensive evaluation suite involving 2,323 natural, challenging, and diverse charts from arXiv papers. CharXiv includes two types of questions: 1) descriptive questions about examining basic chart elements and 2) reasoning questions that require synthesizing information across complex visual elements in the chart. To ensure quality, all charts and questions are handpicked, curated, and verified by human experts. Our results reveal a substantial, previously underestimated gap between the reasoning skills of the strongest proprietary model (i.e., GPT-4o), which achieves 47.1% accuracy, and the strongest open-source model (i.e., InternVL Chat V1.5), which achieves 29.2%. All models lag far behind human performance of 80.5%, underscoring weaknesses in the chart understanding capabilities of existing MLLMs. We hope CharXiv facilitates future research on MLLM chart understanding by providing a more realistic and faithful measure of progress. Project page and leaderboard: https://charxiv.github.io/