MMC: Advancing Multimodal Chart Understanding with Large-scale Instruction Tuning (2311.10774v2)

Abstract: With the rapid development of LLMs and their integration into large multimodal models (LMMs), there has been impressive progress in zero-shot completion of user-oriented vision-language tasks. However, a gap remains in the domain of chart image understanding due to the distinct abstract components in charts. To address this, we introduce a large-scale MultiModal Chart Instruction (\textbf{MMC-Instruction}) dataset comprising 600k instances supporting diverse tasks and chart types. Leveraging this data, we develop MultiModal Chart Assistant (\textbf{MMCA}), an LMM that achieves state-of-the-art performance on existing chart QA benchmarks. Recognizing the need for a comprehensive evaluation of LMM chart understanding, we also propose a MultiModal Chart Benchmark (\textbf{MMC-Benchmark}), a comprehensive human-annotated benchmark with nine distinct tasks evaluating reasoning capabilities over charts. Extensive experiments on MMC-Benchmark reveal the limitations of existing LMMs on correctly interpreting charts, even for the most recent GPT-4V model. Our work provides an instruction-tuning methodology and benchmark to advance multimodal understanding of charts. Code and data are available at https://github.com/FuxiaoLiu/MMC.

Advancing Multimodal Chart Understanding with MMC

The paper "MMC: Advancing Multimodal Chart Understanding with Large-scale Instruction Tuning" introduces a substantial advancement in the domain of multimodal learning specifically focused on chart understanding. The authors address a persistent challenge in the interpretation of chart images by leveraging Large Multimodal Models (LMMs), which integrate the capabilities of LLMs and advanced visual processing techniques.

The paper identifies a gap in current LMM capabilities related to charts, which typically consist of abstract elements like trend lines and legends that differ significantly from natural scene images containing spatially correlated objects. This distinction is crucial because existing models, including prominent ones like GPT-4V, are less adept at discerning the information embodied in charts. In response, the paper presents the MultiModal Chart Instruction (MMC-Instruction) dataset, a vast compilation of 600,000 instances designed to improve chart understanding by including diverse tasks and chart types.

By proposing the MultiModal Chart Assistant (MMCA), the authors effectively demonstrate the utility of their dataset in reaching state-of-the-art performance on chart question-answering benchmarks. Through extensive experimentation, the paper exposes limitations in existing models such as GPT-4V when assessing their capabilities using the newly proposed MultiModal Chart Benchmark (MMC-Benchmark). This benchmark is a meticulously human-annotated framework comprising nine distinct tasks that test reasoning capacities over varied charts.

Key Contributions

1. MMC-Instruction Dataset: This dataset is pivotal in broadening the horizons of multimodal learning by providing a significantly larger and more diverse collection of data than previous datasets. Its instructions and diverse topics enable a more comprehensive tuning process for LMMs.
2. MMCA Model: A novel LMM fine-tuned with the MMC-Instruction dataset, achieving superior results in interpreting chart data compared to existing models. It exemplifies how targeted instruction-tuning can enhance models' comprehension abilities in specific domains.
3. MMC-Benchmark: The benchmark evaluates the chart understanding proficiency of LMMs across a spectrum of tasks, highlighting areas where models, even advanced ones like GPT-4V, struggle. It includes tasks such as chart reasoning, contextual understanding, and chart-to-datatable conversion.

Implications and Future Directions

The implications of this research are manifold. Practically, it extends the utility of LMMs into domains like data analytics, academic research, and business intelligence, where precise chart interpretation is necessary. Theoretically, the paper enriches the landscape of instruction-tuning, setting a precedent for other niche application domains where traditional LLMs or LMMs may fall short.

Future work motivated by this paper might explore integrating these datasets and methodologies into more generalized models or applying similar instruction-tuning paradigms to other types of abstract data representations, such as diagrams or mind maps. Additionally, improvements in OCR integration within LMM architectures could enhance text extraction from graphical elements in charts, further broadening their applicability and accuracy in real-world tasks.

Overall, this paper contributes significantly to the field of AI by initiating advancements in the multimodal understanding of charts, paving the way for future exploration and enhancement of multimodal capabilities in machine learning. The proposed methodologies and datasets form a robust foundation for subsequent research and development efforts aimed at bridging existing gaps in multimodal model performance.