@inproceedings{gong-etal-2023-model,
    title = "Model-Generated Pretraining Signals Improves Zero-Shot Generalization of Text-to-Text Transformers",
    author = "Gong, Linyuan  and
      Xiong, Chenyan  and
      Liu, Xiaodong  and
      Bajaj, Payal  and
      Xie, Yiqing  and
      Cheung, Alvin  and
      Gao, Jianfeng  and
      Song, Xia",
    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.724",
    doi = "10.18653/v1/2023.acl-long.724",
    pages = "12933--12950",
    abstract = "This paper explores the effectiveness of model-generated signals in improving zero-shot generalization of text-to-text Transformers such as T5. We study various designs to pretrain T5 using an auxiliary model to construct more challenging token replacements for the main model to denoise. Key aspects under study include the decoding target, the location of the RTD head, and the masking pattern. Based on these studies, we develop a new model, METRO-T0, which is pretrained using the redesigned ELECTRA-Style pretraining strategies and then prompt-finetuned on a mixture of NLP tasks. METRO-T0 outperforms all similar-sized baselines on prompted NLP benchmarks, such as {\_}T0 Eval{\_} and MMLU, and rivals the state-of-the-art T0-11B model with only **8{\%}** of its parameters. Our analysis on model{'}s neural activation and parameter sensitivity reveals that the effectiveness of METRO-T0 stems from more balanced contribution of parameters and better utilization of their capacity. The code and model checkpoints are available at [\url{https://github.com/gonglinyuan/metro_t0}](\url{https://github.com/gonglinyuan/metro_t0}).",
}

This paper explores the effectiveness of model-generated signals in improving zero-shot generalization of text-to-text Transformers such as T5. We study various designs to pretrain T5 using an auxiliary model to construct more challenging token replacements for the main model to denoise. Key aspects under study include the decoding target, the location of the RTD head, and the masking pattern. Based on these studies, we develop a new model, METRO-T0, which is pretrained using the redesigned ELECTRA-Style pretraining strategies and then prompt-finetuned on a mixture of NLP tasks. METRO-T0 outperforms all similar-sized baselines on prompted NLP benchmarks, such as {\_}T0 Eval{\_} and MMLU, and rivals the state-of-the-art T0-11B model with only **8{\%}** of its parameters. Our analysis on model{'}s neural activation and parameter sensitivity reveals that the effectiveness of METRO-T0 stems from more balanced contribution of parameters and better utilization of their capacity. The code and model checkpoints are available at [\url{https://github.com/gonglinyuan/metro_t0}](\url{https://github.com/gonglinyuan/metro_t0}).
Gong, Linyuan Xiong, Chenyan Liu, Xiaodong Bajaj, Payal Xie, Yiqing Cheung, Alvin Gao, Jianfeng Song, Xia
Model-Generated Pretraining Signals Improves Zero-Shot Generalization of Text-to-Text Transformers