MM-GEN: Enhancing Task Performance Through Targeted Multimodal Data Curation

• URL: http://arxiv.org/abs/2501.04155v1
• Date: Tue, 07 Jan 2025 21:55:56 GMT
• Title: MM-GEN: Enhancing Task Performance Through Targeted Multimodal Data Curation
• Authors: Siddharth Joshi, Besmira Nushi, Vidhisha Balachandran, Varun Chandrasekaran, Vibhav Vineet, Neel Joshi, Baharan Mirzasoleiman,
• Abstract summary: MM-Gen is a scalable method that generates task-specific, high-quality synthetic text for candidate images.<n>Fine-tuning VLMs with data generated by MM-Gen leads to significant performance gains.<n>Compared to human-curated caption data, MM-Gen achieves up to 1.6x better improvements.
• Score: 31.21163360113923
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vision-language models (VLMs) are highly effective but often underperform on specialized tasks; for example, Llava-1.5 struggles with chart and diagram understanding due to scarce task-specific training data. Existing training data, sourced from general-purpose datasets, fails to capture the nuanced details needed for these tasks. We introduce MM-Gen, a scalable method that generates task-specific, high-quality synthetic text for candidate images by leveraging stronger models. MM-Gen employs a three-stage targeted process: partitioning data into subgroups, generating targeted text based on task descriptions, and filtering out redundant and outlier data. Fine-tuning VLMs with data generated by MM-Gen leads to significant performance gains, including 29% on spatial reasoning and 15% on diagram understanding for Llava-1.5 (7B). Compared to human-curated caption data, MM-Gen achieves up to 1.6x better improvements for the original models, proving its effectiveness in enhancing task-specific VLM performance and bridging the gap between general-purpose datasets and specialized requirements. Code available at https://github.com/sjoshi804/MM-Gen.

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