Datasets:
Languages:
English
Size:
100K<n<1M
ArXiv:
Tags:
mixed-modality-retrieval
multimodal-retrieval
visual-document-retrieval
contrastive-learning
training-data
License:
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Browse files
README.md
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<p align="center">
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Official training data for <a href="https://arxiv.org/abs/2610.11816"><i>Chaos in the Text: Revealing the Modality Preference in Mixed-Modality Retrievers</i></a>
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</p>
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<p align="center">
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<a href="https://arxiv.org/abs/2610.11816"><img src="https://img.shields.io/badge/arXiv-2610.11816-b31b1b.svg" alt="arXiv"></a>
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<a href="https://github.com/OpenBMB/Trident"><img src="https://img.shields.io/badge/GitHub-OpenBMB%2FTrident-blue.svg" alt="Code"></a>
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<a href="https://huggingface.co/OpenBMB/Trident-Qwen3VL-2B"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Model-Trident--Qwen3VL--2B-yellow.svg" alt="Model"></a>
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<a href="https://huggingface.co/datasets/OpenBMB/Trident-Train"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Data-Trident--Train-orange.svg" alt="Train data"></a>
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<a href="https://huggingface.co/datasets/OpenBMB/Trident-Eval"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Data-Trident--Eval-orange.svg" alt="Eval data"></a>
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<a href="
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</p>
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## Overview
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Dense retrievers tend to assign higher similarity scores to *text* representations than to semantically equivalent *image* representations, which makes mixed-modality retrieval fragile. Trident-Train is the **modality-balanced** training set used to mitigate this bias.
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If you find this work useful, please cite:
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```bibtex
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@article{
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title
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author
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journal
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year
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url = {https://arxiv.org/abs/2610.11816}
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}
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```
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<p align="center">
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Official training data for <a href="https://arxiv.org/abs/2610.11816"><i>Chaos in the Text: Revealing the Modality Preference in Mixed-Modality Retrievers</i></a>
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</p>
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<p align="center">
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<a href="https://chaos-in-texts.github.io/"><img src="https://img.shields.io/badge/%F0%9F%94%B1%20Project%20Page-chaos--in--texts-5b3fd1.svg" alt="Project Page"></a>
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<a href="https://arxiv.org/abs/2610.11816"><img src="https://img.shields.io/badge/arXiv-2610.11816-b31b1b.svg" alt="arXiv"></a>
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<a href="https://github.com/OpenBMB/Trident"><img src="https://img.shields.io/badge/GitHub-OpenBMB%2FTrident-blue.svg" alt="Code"></a>
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<a href="https://huggingface.co/OpenBMB/Trident-Qwen3VL-2B"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Model-Trident--Qwen3VL--2B-yellow.svg" alt="Model"></a>
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<a href="https://huggingface.co/datasets/OpenBMB/Trident-Train"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Data-Trident--Train-orange.svg" alt="Train data"></a>
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<a href="https://huggingface.co/datasets/OpenBMB/Trident-Eval"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Data-Trident--Eval-orange.svg" alt="Eval data"></a>
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<a href="LICENSE"><img src="https://img.shields.io/badge/license-MIT-green.svg" alt="License"></a>
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</p>
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## Overview
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Dense retrievers tend to assign higher similarity scores to *text* representations than to semantically equivalent *image* representations, which makes mixed-modality retrieval fragile. Trident-Train is the **modality-balanced** training set used to mitigate this bias.
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If you find this work useful, please cite:
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```bibtex
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@article{sun2026chaos,
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title={Chaos in the Text: Revealing the Modality Preference in Mixed-Modality Retrievers},
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author={Sun, Yubo and Peng, Chunyi and Yan, Yukun and Liu, Zhenghao and Xu, Zhipeng and Mei, Sen and Xin, Linlin and Zeng, Zheni and Sun, Maosong},
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journal={arXiv preprint arXiv:2610.11816},
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year={2026}
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}
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```
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