Dynamic and Efficient Inference for Text Generation via BERT Family (2023.acl-long)
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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. |
| Approach: | They propose a novel fine-tuning method which can make a single pre-trained model support Dynamic and Efficient infERence and achieve an adaptive trade-off between model performance and latency. |
| Outcome: | The proposed method achieves higher BLEU scores than the strong autoregressive Transformer model on translation tasks with 3 12 times speedup and faster inference speed compared with the BART model on four GLGE benchmark tasks. |
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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 for text generation use auto-regressive (AR) methods, but inefficient inference is a problem. |
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Distilling Knowledge Learned in BERT for Text Generation (2020.acl-main)
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| Challenge: | Large-scale pre-trained language models such as BERT have revolutionized the state of the art in many language understanding tasks. |
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| Challenge: | Existing techniques for natural language understanding and generation use autoencoding and/or autoregressive objectives to train models. |
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| Challenge: | Generative large language models (LLMs) have significantly influenced various aspects of society, reshaping how we access and interact with information and knowledge. |
| Approach: | They propose a pre-training task that helps BERT-family excel in wider applications . they also explore the integration of cutting-edge technologies into their models to further enhance their capabilities. |
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BERTGen: Multi-task Generation through BERT (2021.acl-long)
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| Challenge: | Recent work in unsupervised and self-supervised pre-training has revolutionised the field of natural language understanding (NLU). |
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| Challenge: | Recent work has explored the suitability of pre-trained language models in low resource settings with less than 1,000 training data points. |
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Do you have the right scissors? Tailoring Pre-trained Language Models via Monte-Carlo Methods (2020.acl-main)
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DagoBERT: Generating Derivational Morphology with a Pretrained Language Model (2020.emnlp-main)
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