Advancing Multimodal Large Language Models in Chart Question Answering with Visualization-Referenced Instruction Tuning

TL;DR

This paper introduces a visualization-referenced instruction tuning method for multimodal large language models to improve chart question answering, addressing data imbalance and chart-specific adaptation issues, resulting in superior performance on benchmarks.

Contribution

The paper proposes a novel data filtering and augmentation pipeline combined with a mixture-of-resolution training strategy to enhance MLLMs for chart question answering tasks.

Findings

Model outperforms state-of-the-art on benchmarks.

Enriched data improves fine-grained visual recognition.

Fewer training examples needed for high performance.

Abstract

Emerging multimodal large language models (MLLMs) exhibit great potential for chart question answering (CQA). Recent efforts primarily focus on scaling up training datasets (i.e., charts, data tables, and question-answer (QA) pairs) through data collection and synthesis. However, our empirical study on existing MLLMs and CQA datasets reveals notable gaps. First, current data collection and synthesis focus on data volume and lack consideration of fine-grained visual encodings and QA tasks, resulting in unbalanced data distribution divergent from practical CQA scenarios. Second, existing work follows the training recipe of the base MLLMs initially designed for natural images, under-exploring the adaptation to unique chart characteristics, such as rich text elements. To fill the gap, we propose a visualization-referenced instruction tuning approach to guide the training dataset enhancement and model development. Specifically, we propose a novel data engine to effectively filter diverse and high-quality data from existing datasets and subsequently refine and augment the data using LLM-based generation techniques to better align with practical QA tasks and visual encodings. Then, to facilitate the adaptation to chart characteristics, we utilize the enriched data to train an MLLM by unfreezing the vision encoder and incorporating a mixture-of-resolution adaptation strategy for enhanced fine-grained recognition. Experimental results validate the effectiveness of our approach. Even with fewer training examples, our model consistently outperforms state-of-the-art CQA models on established benchmarks. We also contribute a dataset split as a benchmark for future research. Source codes and datasets of this paper are available at https://github.com/zengxingchen/ChartQA-MLLM.