#CharXiv: Advancing Multimodal Language Models with Realistic Chart Benchmarks

Multimodal large language models (MLLMs) are crucial for integrating natural language processing (NLP) and computer vision to analyze visual and textual data, especially complex charts in scientific papers and financial reports. However, existing benchmarks lack diversity and realism, leading to an overestimation of MLLM capabilities. To address this, researchers from Princeton University, the University of Wisconsin, and The University of Hong Kong introduced CharXiv, a dataset with 2,323 charts from arXiv papers, covering various subjects and chart types. CharXiv aims to provide a more realistic and challenging evaluation environment for MLLMs by including descriptive and reasoning questions that require detailed analysis of charts.

The evaluation of CharXiv revealed a significant performance gap between open-source and proprietary models, with proprietary models outperforming open-source ones. Models with strong descriptive skills tend to perform better on reasoning tasks, highlighting the importance of descriptive capabilities. The study also identified compositional tasks, like counting labeled ticks on axes, as challenging for MLLMs. CharXiv aims to drive advancements in MLLM capabilities by offering a more accurate assessment of chart interpretation performance.

The findings underscore the need for continued research and improvement in MLLMs, with CharXiv providing a more realistic and challenging dataset for evaluating model performance. By bridging the gap between existing benchmarks and real-world applications, CharXiv aims to enhance the reliability and effectiveness of MLLMs in practical scenarios.

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Source link: https://www.marktechpost.com/2024/06/28/charxiv-a-comprehensive-evaluation-suite-advancing-multimodal-large-language-models-through-realistic-chart-understanding-benchmarks/?amp