CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal LLMs

TL;DR

CharXiv introduces a challenging, human-verified evaluation suite of diverse scientific charts to better measure the reasoning and understanding capabilities of multimodal large language models, revealing significant gaps compared to human performance.

Contribution

This work presents CharXiv, a new comprehensive and challenging dataset for evaluating chart understanding in multimodal LLMs, highlighting current models' weaknesses and providing a more realistic benchmark.

Findings

Proprietary GPT-4o achieves 47.1% accuracy on CharXiv.

Open-source InternVL Chat V1.5 achieves 29.2%.

Humans achieve 80.5%, showing large gaps in model performance.

Abstract

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/