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@@ -119,7 +119,7 @@ Official repository: https://github.com/gonglinyuan/metro_t0
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  # METRO-T0
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- Paper: Model-Generated Pretraining Signals Improves Zero-Shot Generalization of Text-to-Text Transformers (TODO) (ACL 2023)
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  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).
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  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
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  If you find the code and models useful for your research, please cite the following paper:
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  ```
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- TODO
 
 
 
 
 
 
 
 
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  ```
 
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  # METRO-T0
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+ Paper: [Model-Generated Pretraining Signals Improves Zero-Shot Generalization of Text-to-Text Transformers](https://arxiv.org/abs/2305.12567) (ACL 2023)
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  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).
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  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.
 
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  If you find the code and models useful for your research, please cite the following paper:
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  ```
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+ @misc{gong2023modelgenerated,
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+ title={Model-Generated Pretraining Signals Improves Zero-Shot Generalization of Text-to-Text Transformers},
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+ author={Linyuan Gong and Chenyan Xiong and Xiaodong Liu and Payal Bajaj and Yiqing Xie and Alvin Cheung and Jianfeng Gao and Xia Song},
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+ year={2023},
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+ eprint={2305.12567},
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+ archivePrefix={arXiv},
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+ primaryClass={cs.CL},
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+ url={https://arxiv.org/abs/2305.12567}
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+ }
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  ```