Since the release of GPT-4V(O), using them to generate pseudo labels for multi-modality tasks has become more and more popular [1] While we often "stand on the shoulders of giants," the process of building the giant itself--specifically, constructing GPT-4V(O) from its foundational large language model (LLM), GPT-4--remains a mystery. In this work, we explore the potential of using LLMs alone to build a competitive multi-modality model. Given budget constraints, we focus on a specific domain--chart understanding--rather than building a general multi-modality model. Since the quantity and quality of data are key determinants of model performance, this work focuses on building a large-scale chart dataset and applying well-established training pipelines. There are two major challenges in constructing such a dataset: first, collecting a diverse set of chart images, and second, the more critical and difficult task of obtaining high-quality labels for these images.

Multi-modal large language models have demonstrated impressive performances on most vision-language tasks. However, the model generally lacks the understanding capabilities for specific domain data, particularly when it comes to interpreting chart figures. This is mainly due to the lack of relevant multi-modal instruction tuning datasets. In this article, we create a high-quality instruction-tuning dataset leveraging GPT-4. We develop a multi-step data generation process in which different steps are responsible for generating tabular data, creating chart figures, and designing instruction tuning data separately. Our method's flexibility enables us to generate diverse, high-quality instruction-tuning data consistently and efficiently while maintaining a low resource expenditure. Additionally, it allows us to incorporate a wider variety of chart and task types not yet featured in existing datasets. Next, we introduce ChartLlama, a multi-modal large language model that we've trained using our created dataset. ChartLlama outperforms all prior methods in ChartQA, Chart-to-text, and Chart-extraction evaluation benchmarks. Additionally, ChartLlama significantly improves upon the baseline in our specially compiled chart dataset, which includes new chart and task types. The results of ChartLlama confirm the value and huge potential of our proposed data generation method in enhancing chart comprehension.