@inproceedings{guo-etal-2025-mammoth,
    title = "{MA}mmo{TH}-{VL}: Eliciting Multimodal Reasoning with Instruction Tuning at Scale",
    author = "Guo, Jiawei  and
      Zheng, Tianyu  and
      Li, Yizhi  and
      Bai, Yuelin  and
      Li, Bo  and
      Wang, Yubo  and
      Zhu, King  and
      Neubig, Graham  and
      Chen, Wenhu  and
      Yue, Xiang",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.680/",
    pages = "13869--13920",
    ISBN = "979-8-89176-251-0",
    abstract = "Open-source multimodal large language models (MLLMs) have shown significant potential in a broad range of tasks. However, their reasoning capabilities remain constrained by existing instruction-tuning datasets, which were predominately repurposed from academic datasets such as VQA, AI2D, and ChartQA. These datasets target simplistic tasks, and only provide phrase-level answers without any intermediate rationales.To address these challenges, we introduce a scalable and cost-effective method to construct a large-scale multimodal instruction-tuning dataset with rich intermediate rationales designed to elicit CoT reasoning. Using only open models, we create a dataset containing 12M instruction-response pairs to cover diverse reasoning-intensive tasks.Experiments demonstrate that training MLLMs on our dataset not only significantly improves reasoning capabilities, achieving state-of-the-art performance on benchmarks such as MathVerse (+8.1{\%}), MMMU-Pro (+7{\%}), and MuirBench (+13.3{\%}), but also gains improvements of up to 4{\%} on non-reasoning-based benchmarks."
}