ELMER: A Non-Autoregressive Pre-trained Language Model for Efficient and Effective Text Generation (2022.emnlp-main)
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| Challenge: | Existing methods for text generation use auto-regressive (AR) methods, but inefficient inference is a problem. |
| Approach: | They propose an efficient and effective PLM to explicitly model the token dependency during NAR text generation. |
| Outcome: | The proposed model outperforms existing models on three text generation tasks while achieving 10 times faster inference speedup. |
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| Challenge: | Autoregressive generation models generate tokens in a left-to-right, token-by-token fashion, resulting in lag in inference. |
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| Challenge: | Existing methods to improve performance of pre-trained language models are limited due to large-scale parameters and the universal autoregressive decoding paradigm. |
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| Challenge: | Recent advances in image tokenizers have enabled text-to-image generation using auto-regressive methods, but these methods lack pre-trained language models for text-based models. |
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Boxin Wang, Wei Ping, Peng Xu, Lawrence McAfee, Zihan Liu, Mohammad Shoeybi, Yi Dong, Oleksii Kuchaiev, Bo Li, Chaowei Xiao, Anima Anandkumar, Bryan Catanzaro
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| Challenge: | Recent advances in large language models (LLMs) have shown a significant degree of multilingual proficiency on a variety of tasks in multiple languages. |
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