Update README.md

README.md

@@ -119,7 +119,7 @@ Official repository: https://github.com/gonglinyuan/metro_t0
 
 # METRO-T0
 
-Paper: Model-Generated Pretraining Signals Improves Zero-Shot Generalization of Text-to-Text Transformers (TODO) (ACL 2023)
+Paper: [Model-Generated Pretraining Signals Improves Zero-Shot Generalization of Text-to-Text Transformers](https://arxiv.org/abs/2305.12567) (ACL 2023)
 
 METRO-T0 is a T5-style text-to-text Transformer pretrained using model-generated pretraining signals, prompt-finetuned on a family of public NLP tasks proposed in [T0](https://arxiv.org/abs/2110.08207).
 METRO-T0 is highly parameter efficient. For example, METRO-T0-Large++ (775M parameters) outperforms GPT-3 (175B parameters) and T0-3B (3B parameters) on a wide range of NLP tasks.
@@ -165,5 +165,13 @@ print(tokenizer.decode(outputs[0], skip_special_tokens=True))  # expected: posit
 If you find the code and models useful for your research, please cite the following paper:
 
 ```
-TODO
+@misc{gong2023modelgenerated,
+      title={Model-Generated Pretraining Signals Improves Zero-Shot Generalization of Text-to-Text Transformers}, 
+      author={Linyuan Gong and Chenyan Xiong and Xiaodong Liu and Payal Bajaj and Yiqing Xie and Alvin Cheung and Jianfeng Gao and Xia Song},
+      year={2023},
+      eprint={2305.12567},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2305.12567}
+}
 ```