Dataset Card for NoMIRACL

Retrieval Augmented Generation (RAG) is a powerful approach to incorporate external knowledge into large language models (LLMs) to enhance the accuracy and faithfulness of generated responses. However, evaluating LLM robustness in RAG across different language families has been a challenge, leading to gaps in understanding the model's performance against errors in external retrieved knowledge. To address this, we present NoMIRACL, a human-annotated dataset designed for evaluating LLM robustness in RAG across 18 diverse languages.

NoMIRACL includes both a non-relevant and a relevant subset. The non-relevant subset contains queries with all passages manually judged as non-relevant or noisy, while the relevant subset includes queries with at least one judged relevant passage. LLM robustness is measured using two key metrics: hallucination rate and error rate.

All the topics are generated by native speakers of each language from our work in MIRACL, who also label the relevance between the topics and a given document list. The queries with no-relevant documents are used to create the non-relevant subset whereas queries with atleast one relevant document (i.e., queries in MIRACL dev and test) are used to create relevant subset.

This repository contains the topics, qrels and top-10 (maximum) annotated documents of NoMIRACL. The whole collection can be found be here.

Quickstart

import datasets

language = 'german'  # or any of the 18 languages
subset = 'relevant'  # or 'non_relevant'
split = 'test'       # or 'dev' for development split

# four combinations available: 'dev.relevant', 'dev.non_relevant', 'test.relevant' and 'test.non_relevant'
nomiracl = datasets.load_dataset('miracl/nomiracl', language, split=f'{split}.{subset}')

Dataset Structure

1. To download the files:

Under folders data/{lang}, the subset of corpus is saved in .jsonl.gz format, with each line to be:

{"docid": "28742#27", 
"title": "Supercontinent", 
"text": "Oxygen levels of the Archaean Eon were negligible and today they are roughly 21 percent. [ ... ]"}

Under folders data/{lang}/topics, the topics are saved in .tsv format, with each line to be:

qid\tquery

Under folders miracl-v1.0-{lang}/qrels, the qrels are saved in standard TREC format, with each line to be:

qid Q0 docid relevance

2. To access the data using HuggingFace datasets:

import datasets

language = 'german'  # or any of the 18 languages
subset = 'relevant'  # or 'non_relevant'
split = 'test'       # or 'dev' for development split

# four combinations: 'dev.relevant', 'dev.non_relevant', 'test.relevant' and 'test.non_relevant'
nomiracl = datasets.load_dataset('miracl/nomiracl', language, split=f'{split}.{subset}')

# training set:
for data in nomiracl:  # or 'dev', 'testA'
  query_id = data['query_id']
  query = data['query']
  positive_passages = data['positive_passages']
  negative_passages = data['negative_passages']
  
  for entry in positive_passages: # OR 'negative_passages'
    docid = entry['docid']
    title = entry['title']
    text = entry['text']

Dataset Statistics

For NoMIRACL dataset statistics, please refer to our publication here.

Citation Information

@article{thakur2023nomiracl,
  title={NoMIRACL: Knowing When You Don't Know for Robust Multilingual Retrieval-Augmented Generation},
  author={Nandan Thakur and Luiz Bonifacio and Xinyu Zhang and Odunayo Ogundepo and Ehsan Kamalloo and David Alfonso-Hermelo and Xiaoguang Li and Qun Liu and Boxing Chen and Mehdi Rezagholizadeh and Jimmy Lin},
  journal={ArXiv},
  year={2023},
  volume={abs/2312.11361}