--- annotations_creators: - expert-generated language: - ar - bn - en - es - fa - fi - fr - hi - id - ja - ko - ru - sw - te - th - zh multilinguality: - multilingual pretty_name: NoMIRACL size_categories: - 10K ## Quick Overview This repository contains the topics, qrels and top-k (a maximum of 10) annotated passages. The passage collection can be found be here on HF: [miracl/miracl-corpus](https://huggingface.co/datasets/miracl/miracl-corpus). ``` import datasets language = 'german' # or any of the 18 languages (mentioned above in `languages`) subset = 'relevant' # or 'non_relevant' (two subsets: relevant & non-relevant) split = 'test' # or 'dev' for the 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}') ``` ## What is 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 LLM generated responses. However, evaluating query-passage relevance across diverse 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 completely human-annotated dataset designed for evaluating multilingual LLM relevance across 18 diverse languages. NoMIRACL evaluates LLM relevance as a binary classification objective, where it contains two subsets: `non-relevant` and `relevant`. The `non-relevant` subset contains queries with all passages manually judged by an expert assessor as non-relevant, while the `relevant` subset contains queries with at least one judged relevant passage within the labeled passages. LLM relevance is measured using two key metrics: - *hallucination rate* (on the `non-relevant` subset) measuring model tendency to recognize when none of the passages provided are relevant for a given question (non-answerable). - *error rate* (on the `relevant` subset) measuring model tendency as unable to identify relevant passages when provided for a given question (answerable). ## Acknowledgement This dataset would not have been possible without all the topics are generated by native speakers of each language in conjuction from our **multilingual RAG universe** work in part 1, **MIRACL** [[TACL '23]](https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00595/117438/MIRACL-A-Multilingual-Retrieval-Dataset-Covering). The queries with all non-relevant passages are used to create the `non-relevant` subset whereas queries with at least a single relevant passage (i.e., MIRACL dev and test splits) 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](https://huggingface.co/datasets/miracl/miracl-corpus). ## 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 Description * **Website:** https://nomiracl.github.io * **Paper:** https://aclanthology.org/2024.findings-emnlp.730/ * **Repository:** https://github.com/project-miracl/nomiracl ## 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}') # Individual entry in `relevant` or `non_relevant` subset 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 EMNLP 2024 Findings publication. Paper: [https://aclanthology.org/2024.findings-emnlp.730/](https://aclanthology.org/2024.findings-emnlp.730/). ## Citation Information This work was conducted as a collaboration between University of Waterloo and Huawei Technologies. ``` @inproceedings{thakur-etal-2024-knowing, title = "{``}Knowing When You Don{'}t Know{''}: A Multilingual Relevance Assessment Dataset for Robust Retrieval-Augmented Generation", author = "Thakur, Nandan and Bonifacio, Luiz and Zhang, Crystina and Ogundepo, Odunayo and Kamalloo, Ehsan and Alfonso-Hermelo, David and Li, Xiaoguang and Liu, Qun and Chen, Boxing and Rezagholizadeh, Mehdi and Lin, Jimmy", editor = "Al-Onaizan, Yaser and Bansal, Mohit and Chen, Yun-Nung", booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024", month = nov, year = "2024", address = "Miami, Florida, USA", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2024.findings-emnlp.730", pages = "12508--12526", abstract = "Retrieval-Augmented Generation (RAG) grounds Large Language Model (LLM) output by leveraging external knowledge sources to reduce factual hallucinations. However, prior work lacks a comprehensive evaluation of different language families, making it challenging to evaluate LLM robustness against errors in external retrieved knowledge. To overcome this, we establish **NoMIRACL**, a human-annotated dataset for evaluating LLM robustness in RAG across 18 typologically diverse languages. NoMIRACL includes both a non-relevant and a relevant subset. Queries in the non-relevant subset contain passages judged as non-relevant, whereas queries in the relevant subset include at least a single judged relevant passage. We measure relevance assessment using: (i) *hallucination rate*, measuring model tendency to hallucinate when the answer is not present in passages in the non-relevant subset, and (ii) *error rate*, measuring model inaccuracy to recognize relevant passages in the relevant subset. In our work, we observe that most models struggle to balance the two capacities. Models such as LLAMA-2 and Orca-2 achieve over 88{\%} hallucination rate on the non-relevant subset. Mistral and LLAMA-3 hallucinate less but can achieve up to a 74.9{\%} error rate on the relevant subset. Overall, GPT-4 is observed to provide the best tradeoff on both subsets, highlighting future work necessary to improve LLM robustness. NoMIRACL dataset and evaluation code are available at: https://github.com/project-miracl/nomiracl.", } ```