modified README.md

README.md

@@ -34,29 +34,47 @@ task_categories:
 
 license:
 - apache-2.0
-
-task_ids:
-- document-retrieval
 ---
 
 # 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.
 
-## Dataset Description
-* **Repository:** https://github.com/project-miracl/nomiracl
-* **Paper:** https://arxiv.org/abs/2312.11361
+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.
 
-<!-- MIRACL 🌍🙌🌏 (Multilingual Information Retrieval Across a Continuum of Languages) is a multilingual retrieval dataset that focuses on search across 18 different languages, which collectively encompass over three billion native speakers around the world.
+All the topics are generated by native speakers of each language from our work in [MIRACL](https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00595/117438/MIRACL-A-Multilingual-Retrieval-Dataset-Covering), 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 dataset contains the collection data of the 16 "known languages". The remaining 2 "surprise languages" will not be released until later.
+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
 
-The topics are generated by native speakers of each language, who also label the relevance between the topics and a given document list.
+language = 'german'  # or any of the 18 languages
+subset = 'relevant'  # or 'non_relevant'
+split = 'test'       # or 'dev' for development split
 
-This repository only contains the topics and qrels of MIRACL. The collection can be found [here](https://huggingface.co/datasets/miracl/miracl-corpus).
+nomiracl = datasets.load_dataset('miracl/nomiracl', language, split=f'{split}.{subset})
+```
+
+
+## Dataset Description
+* **Repository:** https://github.com/project-miracl/nomiracl
+* **Paper:** https://arxiv.org/abs/2312.11361
 
 ## Dataset Structure
 1. To download the files:
-Under folders `miracl-v1.0-{lang}/topics`,
+
+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
@@ -68,14 +86,19 @@ 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`:
 ```
-lang='ar'  # or any of the 16 languages
-miracl = datasets.load_dataset('miracl/miracl', lang, use_auth_token=True)
+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 miracl['train']:  # or 'dev', 'testA'
+for data in nomiracl:  # or 'dev', 'testA'
   query_id = data['query_id']
   query = data['query']
   positive_passages = data['positive_passages']
@@ -86,30 +109,17 @@ for data in miracl['train']:  # or 'dev', 'testA'
     title = entry['title']
     text = entry['text']
 ```
-The structure is the same for `train`, `dev`, and `testA` set, where `testA` only exists for languages in Mr. TyDi (i.e., Arabic, Bengali, English, Finnish, Indonesian, Japanese, Korean, Russian, Swahili, Telugu, Thai).
-Note that `negative_passages` are annotated by native speakers as well, instead of the non-positive passages from top-`k` retrieval results.
-
 
 ## Dataset Statistics 
-The following table contains the number of queries (`#Q`) and the number of judgments (`#J`) in each language, for the training and development set,
-where the judgments include both positive and negative samples.
-
-| Lang | Train |        |  Dev  |        |
-|:----:|:-----:|:------:|:-----:|:------:|
-|      | **#Q**| **#J** |**#Q** |**#J**  |
-|  ar  | 3,495 | 25,382 | 2,896 | 29,197 |
-|  bn  | 1,631 | 16,754 |   411 |  4,206 |
-|  en  | 2,863 | 29,416 |   799 |  8,350 |
-|  es  | 2,162 | 21,531 |   648 |  6,443 |
-|  fa  | 2,107 | 21,844 |   632 |  6,571 |
-|  fi  | 2,897 | 20,350 | 1,271 | 12,008 |
-|  fr  | 1,143 | 11,426 |   343 |  3,429 |
-|  hi  | 1,169 | 11,668 |   350 |  3,494 |
-|  id  | 4,071 | 41,358 |   960 |  9,668 |
-|  ja  | 3,477 | 34,387 |   860 |  8,354 |
-|  ko  |   868 | 12,767 |   213 |  3,057 |
-|  ru  | 4,683 | 33,921 | 1,252 | 13,100 |
-|  sw  | 1,901 |  9,359 |   482 |  5,092 |
-|  te  | 3,452 | 18,608 |   828 |  1,606 |
-|  th  | 2,972 | 21,293 |   733 |  7,573 |
-|  zh  | 1,312 | 13,113 |   393 |  3,928 | -->
+For NoMIRACL dataset statistics, please refer to our publication [here](https://arxiv.org/abs/2312.11361).
+
+
+## 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}
+```