X-CoT: Explainable Text-to-Video Retrieval Dataset

This repository contains the dataset for X-CoT: Explainable Text-to-Video Retrieval via LLM-based Chain-of-Thought Reasoning.

This dataset expands existing text-to-video retrieval benchmarks with additional video annotations to support semantic understanding and reduce data bias. It is designed to facilitate explainable retrieval frameworks based on LLM Chain-of-Thought reasoning, aiming to improve retrieval performance and provide detailed rationales for ranking results.

Download

pip install huggingface_hub

from huggingface_hub import hf_hub_download

REPO = "prasannareddyp/X-CoT"

# Everything (all four datasets, all retrievers)
snapshot_download(repo_id=REPO, repo_type="dataset", local_dir="./")

# Individual files

# X-Pool first-stage ranking for MSVD
hf_hub_download(repo_id=REPO, filename="outputs/MSVD/xpool_ranking_benchmark.jsonl",
                repo_type="dataset", local_dir="./")

# Video breakdowns (the LLM-generated annotations) for MSVD
hf_hub_download(repo_id=REPO, filename="outputs/MSVD/video_breakdowns_benchmark.jsonl",
                repo_type="dataset", local_dir="./")

# Files land at ./outputs/<DATASET>/<file>.jsonl — the layout the X-CoT code expects.

Paper

X-CoT: Explainable Text-to-Video Retrieval via LLM-based Chain-of-Thought Reasoning

Project Page

https://prasannapulakurthi.github.io/X-CoT/

Code

https://github.com/PrasannaPulakurthi/X-CoT

Citation

If you find this work valuable for your research, we kindly request that you cite the following paper:

@inproceedings{pulakurthi2025x,
  title={X-CoT: Explainable Text-to-Video Retrieval via LLM-based Chain-of-Thought Reasoning},
  author={Pulakurthi, Prasanna Reddy and Wang, Jiamian and Rabbani, Majid and Dianat, Sohail and Rao, Raghuveer and Tao, Zhiqiang},
  booktitle={Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing},
  pages={31172--31183},
  year={2025}
}