TaskGalaxy: Scaling Multi-modal Instruction Fine-tuning with Tens of Thousands Vision Task Types

Jiankang Chen, Tianke Zhang, Changyi Liu, Haojie Ding, Yaya Shi, cheng.feng, Huihui Xiao, Bin Wen, Fan Yang, Tingting Gao, Di ZHANG

Keywords: instruction fine-tunning dataset, multimodal large language models, hierarchical task types, data generation pipeline

TL;DR: We introduce TaskGalaxy, a large multi-modal instruction fine-tuning dataset containing over 19,000 hierarchical task types, which has demonstrated significant improvements in the performance of vision-language models across various benchmarks.

Abstract: Multimodal visual language models are gaining prominence in open-world applications, driven by advancements in model architectures, training techniques, and high-quality data. However, their performance is often limited by insufficient task-specific data, leading to poor generalization and biased outputs. Existing efforts to increase task diversity in fine-tuning datasets are hindered by the labor-intensive process of manual task labeling, which typically produces only a few hundred task types. To address this, we propose TaskGalaxy, a large-scale multimodal instruction fine-tuning dataset comprising 19,227 hierarchical task types and 413,648 samples. TaskGalaxy utilizes GPT-4o to enrich task diversity by expanding from a small set of manually defined tasks, with CLIP and GPT-4o filtering those that best match open-source images, and generating relevant question-answer pairs. Multiple models are employed to ensure sample quality. This automated process enhances both task diversity and data quality, reducing manual intervention. Incorporating TaskGalaxy into LLaVA-v1.5 and InternVL-Chat-v1.0 models shows substantial performance improvements across 16 benchmarks, demonstrating the critical importance of task diversity. TaskGalaxy is publicly released at https://github.com/Kwai-YuanQi/TaskGalaxy.

Primary Area: datasets and benchmarks

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Submission Number: 2956