Trident-Train

Official training data for Chaos in the Text: Revealing the Modality Preference in Mixed-Modality Retrievers

Overview

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.

Every training instance pairs one query with three co-equal positive views of the same document:

View	Content	How it is built
Text	A detailed description of the document image	Generated by Qwen3-VL-235B-A22B-Instruct
Image	The original document image	Original image
Fused	A short summary + the original image	Summary generated by Qwen3-VL-235B-A22B-Instruct

The three views are optimized jointly with the Multi-Positive View InfoNCE loss (see the paper and the code).

Three training files are provided:

File	Domain	Source	#Queries
train_vdoc.jsonl	Visual documents	OpenDocVQA	~41K
train_nature.jsonl	Natural images	MMEB training sets	~170K
train_all.jsonl	Visual documents + natural images	Union of the two files above	~211K

train_all.jsonl is the data used to train the released Trident-Qwen3VL-2B. The two domain files can be used to train domain-specific retrievers.

Directory Structure

Trident-Train/
├── README.md
├── train_all.jsonl
├── train_vdoc.jsonl
├── train_nature.jsonl
└── images/
    ├── aokvqa.tar.gz
    ├── docvqa.tar.gz
    ├── dude.tar.gz
    ├── fvqa.tar.gz
    ├── infovqa.tar.gz
    ├── mscoco.tar.gz
    ├── openwikitable.tar.gz
    ├── render_images.tar.gz
    ├── visualmrc.tar.gz
    ├── visualnews.tar.gz
    └── wiki.tar.gz

After downloading and extracting, the layout matches what the Trident code expects under data/:

data/
└── Train/
    ├── train_all.jsonl
    ├── train_vdoc.jsonl
    ├── train_nature.jsonl
    └── images/
        ├── docvqa/ ...
        ├── mscoco/ ...
        └── ...

Image archives

Archive	Source dataset
aokvqa.tar.gz	A-OKVQA
docvqa.tar.gz	DocVQA
dude.tar.gz	DUDE
fvqa.tar.gz	FVQA
infovqa.tar.gz	InfographicVQA
mscoco.tar.gz	MSCOCO
openwikitable.tar.gz	OpenWikiTable
render_images.tar.gz	Rendered images generated for the training data
visualmrc.tar.gz	VisualMRC
visualnews.tar.gz	VisualNews
wiki.tar.gz	Wikipedia images

Each archive extracts into images/ and keeps its own sub-folder, so image paths in the .jsonl files resolve as data/Train/images/<path>.

Download & Extract

Option 1: one-line script (recommended)

The Trident repository ships a helper script that downloads and extracts the data in the expected layout:

git clone https://github.com/OpenBMB/Trident.git && cd Trident

bash data/download_data.sh train                              # everything (train_all.jsonl + all images)
bash data/download_data.sh train --train-subset vdoc          # only train_vdoc.jsonl
bash data/download_data.sh train --train-images docvqa,dude   # only some image archives
bash data/download_data.sh train --mirror                     # use hf-mirror.com

Option 2: manual

# 1. Download (everything, or pass --include "train_vdoc.jsonl" "images/docvqa.tar.gz" for a subset)
hf download OpenBMB/Trident-Train \
    --repo-type dataset --local-dir data/Train

# 2. Extract the image archives in place
for f in data/Train/images/*.tar.gz; do
    tar -xzf "$f" -C data/Train/images
done

# 3. (optional) remove archives to save disk space
# rm data/Train/images/*.tar.gz

Or download from Python:

from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="OpenBMB/Trident-Train",
    repo_type="dataset",
    local_dir="data/Train",
    allow_patterns=["train_vdoc.jsonl", "images/docvqa.tar.gz"],   # omit to download everything
)

Then train with the code repository, e.g.:

bash scripts/train.sh configs/trident_qwen3vl.yaml \
     --set data.train_path=data/Train/train_all.jsonl

Data Format

Every .jsonl file contains one JSON object per line. Image paths are relative to images/ and use forward slashes.

{"query": [{"text": "How many stores did Saint Laurent operate in Western Europe in 2020?"}],
 "positive": [{"text": "<detailed description>", "id": "d1_text"},
              {"image": "docvqa/d1.png", "id": "d1_image"},
              {"text": "<short summary>", "image": "docvqa/d1.png", "id": "d1_fused"}],
 "doc_id": "d1", "query_id": "q1", "task": "default"}

Field	Description
query	A list with the query content (text and/or image).
positive	Exactly three positive views of the same document, always in the order text → image → fused. Each view carries its own id.
doc_id, query_id	Identities of the document and the query. Used to mask false negatives inside a batch.
task	Task tag ("default").
positive_ids, group_id	Optional. Extra identity fields for false-negative masking. Missing ids fall back to content hashes.

View	Fields present
Text	id, text
Image	id, image
Fused (summary + image)	id, text, image

See src/mmemb/data/schema.py in the code repository for all supported fields.

All ids (id, doc_id, query_id) are strings.

Construction

Following Section 4.1 of the paper, we build training instances from OpenDocVQA (visual documents) and the MMEB training sets (natural images). For every image document, Qwen3-VL-235B-A22B-Instruct generates (i) a detailed description (Text view) and (ii) a short summary that is paired with the image (Fused view). The original image is kept as the Image view, so each document is represented by three equally weighted positive views.

License

The annotations released here are under the MIT License. The images originate from third-party datasets (see Image archives) and remain subject to their original licenses; please make sure your use complies with them.

Citation

If you find this work useful, please cite:

@article{trident2026chaos,
  title   = {Chaos in the Text: Revealing the Modality Preference in Mixed-Modality Retrievers},
  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},
  journal = {arXiv preprint arXiv:2610.11816},
  year    = {2026},
  url     = {https://arxiv.org/abs/2610.11816}
}