Mastering Collaborative Multi-modal Data Selection: A Focus on Informativeness, Uniqueness, and Representativeness

• URL: http://arxiv.org/abs/2412.06293v1
• Date: Mon, 09 Dec 2024 08:36:10 GMT
• Title: Mastering Collaborative Multi-modal Data Selection: A Focus on Informativeness, Uniqueness, and Representativeness
• Authors: Qifan Yu, Zhebei Shen, Zhongqi Yue, Yang Wu, Wenqiao Zhang, Yunfei Li, Juncheng Li, Siliang Tang, Yueting Zhuang,
• Abstract summary: We argue that a valuable sample should be informative of the task, non-redundant, and represent the sample distribution (i.e., not an outlier) We propose a collaborative framework, DataTailor, which leverages three key principles--informativeness, uniqueness, and representativeness--for effective data selection. Experiments on various benchmarks demonstrate that DataTailor achieves 100.8% of the performance of full-data fine-tuning with only 15% of the data.
• Score: 65.01625761120924
• License:
• Abstract: Instruction tuning fine-tunes pre-trained Multi-modal Large Language Models (MLLMs) to handle real-world tasks. However, the rapid expansion of visual instruction datasets introduces data redundancy, leading to excessive computational costs. We propose a collaborative framework, DataTailor, which leverages three key principles--informativeness, uniqueness, and representativeness--for effective data selection. We argue that a valuable sample should be informative of the task, non-redundant, and represent the sample distribution (i.e., not an outlier). We further propose practical ways to score against each principle, which automatically adapts to a given dataset without tedious hyperparameter tuning. Comprehensive experiments on various benchmarks demonstrate that DataTailor achieves 100.8% of the performance of full-data fine-tuning with only 15% of the data, significantly reducing computational costs while maintaining superior results. This exemplifies the "Less is More" philosophy in MLLM development.

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