On Pre-training of Multimodal Language Models Customized for Chart Understanding

TL;DR

This paper enhances multimodal language models for scientific chart understanding by integrating raw data, textual representations, and data extraction steps, resulting in a specialized model called CHOPINLLM that outperforms existing approaches.

Contribution

It introduces novel training strategies for MLLMs to improve chart comprehension, including data alignment, textual image replacement, and data extraction methods, culminating in the CHOPINLLM model.

Findings

CHOPINLLM effectively interprets various chart types.

Incorporating raw data improves comprehension accuracy.

Textual representation transfer enhances reasoning skills.

Abstract

Recent studies customizing Multimodal Large Language Models (MLLMs) for domain-specific tasks have yielded promising results, especially in the field of scientific chart comprehension. These studies generally utilize visual instruction tuning with specialized datasets to enhance question and answer (QA) accuracy within the chart domain. However, they often neglect the fundamental discrepancy between natural image-caption pre-training data and digital chart image-QA data, particularly in the models' capacity to extract underlying numeric values from charts. This paper tackles this oversight by exploring the training processes necessary to improve MLLMs' comprehension of charts. We present three key findings: (1) Incorporating raw data values in alignment pre-training markedly improves comprehension of chart data. (2) Replacing images with their textual representation randomly during end-to-end fine-tuning transfer the language reasoning capability to chart interpretation skills. (3) Requiring the model to first extract the underlying chart data and then answer the question in the fine-tuning can further improve the accuracy. Consequently, we introduce CHOPINLLM, an MLLM tailored for in-depth chart comprehension. CHOPINLLM effectively interprets various types of charts, including unannotated ones, while maintaining robust reasoning abilities. Furthermore, we establish a new benchmark to evaluate MLLMs' understanding of different chart types across various comprehension levels. Experimental results show that CHOPINLLM exhibits strong performance in understanding both annotated and unannotated charts across a wide range of types.