Add paper

README.md

@@ -667,7 +667,7 @@ model-index:
 > We present BLOOMZ & mT0, a family of models capable of following human instructions in dozens of languages zero-shot. We finetune BLOOM & mT5 pretrained multilingual language models on our crosslingual task mixture (xP3) and find the resulting models capable of crosslingual generalization to unseen tasks & languages.
 
 - **Repository:** [bigscience-workshop/xmtf](https://github.com/bigscience-workshop/xmtf)
-- **Paper:** [TODO]
+- **Paper:** [Crosslingual Generalization through Multitask Finetuning](https://arxiv.org/abs/2211.01786)
 - **Point of Contact:** [Niklas Muennighoff](mailto:niklas@hf.co)
 - **Languages:** Refer to [bloom](https://huggingface.co/bigscience/bloom) for pretraining & [xP3](https://huggingface.co/datasets/bigscience/xP3) for finetuning language proportions. It understands both pretraining & finetuning languages.
 - **BLOOMZ & mT0 Model Family:**
@@ -865,7 +865,7 @@ print(tokenizer.decode(outputs[0]))
 
 # Evaluation
 
-We refer to Table 7 from our paper [TODO LINK] & [bigscience/evaluation-results](https://huggingface.co/datasets/bigscience/evaluation-results) for zero-shot results on unseen tasks. The sidebar reports zero-shot performance of the best prompt per dataset config.
+We refer to Table 7 from our [paper](https://arxiv.org/abs/2211.01786) & [bigscience/evaluation-results](https://huggingface.co/datasets/bigscience/evaluation-results) for zero-shot results on unseen tasks. The sidebar reports zero-shot performance of the best prompt per dataset config.
 
 # Citation
 ```bibtex