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---
dataset_info:
features:
- name: query_id
dtype: string
- name: query
dtype: string
- name: positive_passages
list:
- name: docid
dtype: string
- name: text
dtype: string
- name: title
dtype: string
- name: negative_passages
list:
- name: docid
dtype: string
- name: text
dtype: string
- name: title
dtype: string
- name: subset
dtype: string
splits:
- name: train
num_bytes: 10961970907
num_examples: 648766
download_size: 6447294919
dataset_size: 10961970907
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: cc-by-sa-4.0
task_categories:
- question-answering
language:
- en
pretty_name: RLHN-680K
size_categories:
- 100K<n<1M
---
# Dataset Card for RLHN-680K
## Dataset Description
[Repository](https://github.com/castorini/rlhn) |
[Paper](https://huggingface.co/papers/2505.16967) |
[ArXiv](https://arxiv.org/abs/2505.16967)
RLHN is a cascading LLM framework designed to accurately relabel hard negatives in existing IR/RAG training datasets, such as MS MARCO and HotpotQA.
This Tevatron dataset (680K training pairs) contains the queries, positives + relabeled hard negatives, remaining hard negatives for 7 datasets in the BGE training collection.
This repository contains the training pairs that can be used to fine-tune embedding, ColBERT or multi-vector, and reranker models.
The original dataset (bad quality; containing false negatives) can be found at [rlhn/default-680K](https://huggingface.co/datasets/rlhn/default-680K/).
> Note: RLHN datasets are not **new** training datasets, but rather existing BGE collection training datasets with hard negatives cleaned!
## Dataset Structure
To access the data using HuggingFace `datasets`:
```python
rlhn = datasets.load_dataset('rlhn/rlhn-680K')
# training set:
for data in freshstack['train']:
query_id = data["query_id"] # md5 hash of the query_id
query = data["query"] # query text
subset = data["subset"] # training dataset, e.g., fiqa or msmarco_passage
# positive passages
for positive_passage in data["positive_passages"]:
doc_id = positive_passage["docid"]
title = positive_passage["title"] # title is usually empty, added in text
text = positive_passage["text"] # contains both the title & text
# hard negative passages
for negative_passage in data["negative_passages"]:
doc_id = negative_passage["docid"]
title = negative_passage["title"] # title is usually empty, added in text
text = negative_passage["text"] # contains both the title & text
```
## Original Dataset Statistics
The following table contains the number of training pairs for each training dataset included in RLHN. These numbers are for the default setting.
| Dataset | 100K splits | 250K splits | 400K splits | 680K splits |
|-------------------|-------------|-------------|-------------|------------- |
| arguana | 4,065 | 4,065 | 4,065 | 4,065 |
| fever | 28,755 | 28,755 | 28,755 | 28,755 |
| fiqa | 5,500 | 5,500 | 5,500 | 5,500 |
| hotpotqa | 10,250 | 30,000 | 84,516 | 84,516 |
| msmarco_passage | 49,571 | 145,000 | 210,000 | 485,823 |
| nq | 6,110 | 30,000 | 58,568 | 58,568 |
| scidocsrr | 12,654 | 12,654 | 12,654 | 12,654 |
| **total** | **96,167** | **255,974** | **404,058** | **679,881** |
## License
The RLHN dataset is made available with the CC-BY-SA 4.0 license.
## Hashing & IDs
We generate the md5 hash as the unique identifier (ID) for both the query \& documents, using the code below:
```python
import hashlib
def get_md5_hash(text):
"""Calculates the MD5 hash of a given string.
Args:
text: The string to hash.
Returns:
The MD5 hash of the string as a hexadecimal string.
"""
text_bytes = text.encode('utf-8') # Encode the string to bytes
md5_hash = hashlib.md5(text_bytes).hexdigest()
return md5_hash
```
## Citation
```
@misc{thakur2025relabel,
title={Fixing Data That Hurts Performance: Cascading LLMs to Relabel Hard Negatives for Robust Information Retrieval},
author={Nandan Thakur and Crystina Zhang and Xueguang Ma and Jimmy Lin},
year={2025},
eprint={2505.16967},
archivePrefix={arXiv},
primaryClass={cs.IR},
url={https://arxiv.org/abs/2505.16967},
}
``` |