Update README.md

README.md

@@ -65,4 +65,32 @@ Arguments:
 - MODEL_NAME: name of the model to fine-tune, can be a local path or a model from the [HuggingFace Model Hub](https://huggingface.co/models)
 - TASK_NAME: one of [`ner, paraphrase, qa, sentiment, xcopa, xnli, flores`]
 
-> For MASSIVE task, please use the instrction provided in the [official repository](https://github.com/alexa/massive) 
+> For MASSIVE task, please use the instrction provided in the [official repository](https://github.com/alexa/massive)
+
+
+## Citation
+
+```
+@inproceedings{doddapaneni-etal-2023-towards,
+    title = "Towards Leaving No {I}ndic Language Behind: Building Monolingual Corpora, Benchmark and Models for {I}ndic Languages",
+    author = "Doddapaneni, Sumanth  and
+      Aralikatte, Rahul  and
+      Ramesh, Gowtham  and
+      Goyal, Shreya  and
+      Khapra, Mitesh M.  and
+      Kunchukuttan, Anoop  and
+      Kumar, Pratyush",
+    editor = "Rogers, Anna  and
+      Boyd-Graber, Jordan  and
+      Okazaki, Naoaki",
+    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
+    month = jul,
+    year = "2023",
+    address = "Toronto, Canada",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2023.acl-long.693",
+    doi = "10.18653/v1/2023.acl-long.693",
+    pages = "12402--12426",
+    abstract = "Building Natural Language Understanding (NLU) capabilities for Indic languages, which have a collective speaker base of more than one billion speakers is absolutely crucial. In this work, we aim to improve the NLU capabilities of Indic languages by making contributions along 3 important axes (i) monolingual corpora (ii) NLU testsets (iii) multilingual LLMs focusing on Indic languages. Specifically, we curate the largest monolingual corpora, IndicCorp, with 20.9B tokens covering 24 languages from 4 language families - a 2.3x increase over prior work, while supporting 12 additional languages. Next, we create a human-supervised benchmark, IndicXTREME, consisting of nine diverse NLU tasks covering 20 languages. Across languages and tasks, IndicXTREME contains a total of 105 evaluation sets, of which 52 are new contributions to the literature. To the best of our knowledge, this is the first effort towards creating a standard benchmark for Indic languages that aims to test the multilingual zero-shot capabilities of pretrained language models. Finally, we train IndicBERT v2, a state-of-the-art model supporting all the languages. Averaged across languages and tasks, the model achieves an absolute improvement of 2 points over a strong baseline. The data and models are available at \url{https://github.com/AI4Bharat/IndicBERT}.",
+}
+```