MLLM-Selector: Necessity and Diversity-driven High-Value Data Selection for Enhanced Visual Instruction Tuning

• URL: http://arxiv.org/abs/2503.20502v2
• Date: Sun, 30 Mar 2025 03:54:36 GMT
• Title: MLLM-Selector: Necessity and Diversity-driven High-Value Data Selection for Enhanced Visual Instruction Tuning
• Authors: Yiwei Ma, Guohai Xu, Xiaoshuai Sun, Jiayi Ji, Jie Lou, Debing Zhang, Rongrong Ji,
• Abstract summary: We introduce MLLM-Selector, an automated approach that identifies valuable data for visual instruction tuning.<n>We calculate necessity scores for each sample in the VIT data pool to identify samples pivotal for enhancing model performance.<n>Our findings underscore the importance of mixing necessity and diversity in data choice, leading to the creation of MLLM-Selector.
• Score: 69.7347209018861
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visual instruction tuning (VIT) has emerged as a crucial technique for enabling multi-modal large language models (MLLMs) to follow user instructions adeptly. Yet, a significant gap persists in understanding the attributes of high-quality instruction tuning data and frameworks for its automated selection. To address this, we introduce MLLM-Selector, an automated approach that identifies valuable data for VIT by weighing necessity and diversity. Our process starts by randomly sampling a subset from the VIT data pool to fine-tune a pretrained model, thus creating a seed model with an initial ability to follow instructions. Then, leveraging the seed model, we calculate necessity scores for each sample in the VIT data pool to identify samples pivotal for enhancing model performance. Our findings underscore the importance of mixing necessity and diversity in data choice, leading to the creation of MLLM-Selector, our methodology that fuses necessity scoring with strategic sampling for superior data refinement. Empirical results indicate that within identical experimental conditions, MLLM-Selector surpasses LLaVA-1.5 in some benchmarks with less than 1% of the data and consistently exceeds performance across all validated benchmarks when using less than 50%.

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