KILT Benchmark with Top-k Retrieval Results

This dataset is a modified version of the KILT Benchmark from the paper "KILT: a benchmark for knowledge intensive language tasks". It includes additional top-k retrieval results used in the paper "Chain-of-Retrieval Augmented Generation".

Differences from the Original KILT Dataset

The primary difference is the addition of the context_doc_ids field. This field provides the IDs of the top-k documents retrieved during the CoRAG experiments. You can use these IDs to retrieve the corresponding document content from the corag/kilt-corpus dataset at https://huggingface.co/datasets/corag/kilt-corpus.

Fields

• query_id: The ID of the query.
• query: The text of the query.
• answers: The answers to the query.
• context_doc_ids: The IDs of the top-k documents retrieved for the query.

Usage

To use this dataset, you can load it using the Hugging Face datasets library:

from datasets import load_dataset

dataset = load_dataset("corag/kilt", "hotpotqa", split="train")

print(dataset)
print(dataset[0])

Caveats

1. Due to issues with ID mapping, a small number of questions are missing from the TriviaQA dataset. However, this is expected to have a negligible impact on overall evaluation metrics.

References

@inproceedings{petroni2021kilt,
  title={KILT: a benchmark for knowledge intensive language tasks},
  author={Petroni, Fabio and Lewis, Patrick and Rockt{\"a}schel, Tim and Riedel, Sebastian and Yih, Wen-tau},
  booktitle={Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
  pages={1113--1131},
  year={2021}
}

@article{wang2025chain,
  title={Chain-of-Retrieval Augmented Generation},
  author={Wang, Liang and Chen, Haonan and Yang, Nan and Huang, Xiaolong and Dou, Zhicheng and Wei, Furu},
  journal={arXiv preprint arXiv:2501.14342},
  year={2025}
}