Summary Efficient Transformer Knowledge Distillation A Performance Review arxiv.org
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One Line
This study examines how knowledge distillation affects efficient attention transformers in pretrained language models, with a focus on introducing a new NER dataset.
Slides
Slide Presentation (10 slides)
Key Points
- Model compression and efficient attention mechanisms are focused on in this study.
- Knowledge distillation is used to compress efficient attention transformers while preserving performance.
- Efficient attention models allow for processing longer sequences with reduced computational overhead.
- Knowledge distillation is a technique that involves training a larger model and distilling its knowledge into a smaller model.
- The combination of knowledge distillation and efficient attention architectures results in compressed models with preserved performance and reduced inference times.
- The GONERD dataset is introduced to evaluate the performance of NER models on long-context sequences.
- Knowledge distillation improves NER performance on both CoNLL-2003 and GONERD datasets.
- Further research is needed to explore distillation methods tailored for specific efficient attention mechanisms, tasks, and architectures.
Summaries
19 word summary
This study assesses knowledge distillation's impact on efficient attention transformers in pretrained language models, highlighting a new NER dataset.
51 word summary
This study evaluates the performance of knowledge distillation on efficient attention transformers in pretrained transformer language models. Distilled efficient attention transformers can maintain the original model's performance while reducing inference times. The researchers introduce a new long-context Named Entity Recognition (NER) dataset called GONERD, addressing a gap in long-context NER benchmarking.
131 word summary
This study examines the combination of model compression and efficient attention mechanisms in pretrained transformer language models. The researchers evaluate the performance of knowledge distillation on efficient attention transformers and introduce a new long-context Named Entity Recognition (NER) dataset called GONERD. Distilled efficient attention transformers can maintain a significant amount of the original model's performance while reducing inference times. Transformer-based models like BERT and RoBERTa struggle with processing long-context sequences, leading to the development of efficient attention transformer models. However, these models are still computationally expensive to train and deploy. Therefore, the researchers explore the combination of knowledge distillation and efficient attention architectures. The results demonstrate that distilled efficient attention models can preserve performance while reducing inference times. The introduction of the GONERD dataset fills a gap in long-context NER benchmarking.
441 word summary
This study explores the combination of model compression and efficient attention mechanisms in pretrained transformer language models. The researchers evaluate the performance of knowledge distillation on efficient attention transformers and introduce a new long-context Named Entity Recognition (NER) dataset called GONERD. The results show that distilled efficient attention transformers can maintain a significant amount of the original model's performance while reducing inference times. This demonstrates that knowledge distillation is an effective method for creating high-performing efficient attention models with low costs.
Transformer-based models like BERT and RoBERTa have achieved state-of-the-art performance in Natural Language Processing (NLP) tasks but struggle with processing long-context sequences due to their limited input length. Efficient attention transformer models, such as Longformer, Big Bird, Nystromformer, and LSG, have been developed to address this limitation by accepting longer sequences with reduced computational overhead.
While efficient attention models require less computational resources than non-efficient models, they are still computationally expensive to train and deploy. This leads to increased operational costs and difficulty deploying the models in resource-limited or low internet access scenarios. In response, the NLP community has been exploring cheaper yet performant models created through Knowledge Distillation (KD).
Knowledge Distillation involves training a larger, complex model (teacher model) and distilling its knowledge into a smaller, simpler model (student model). This technique has successfully compressed BERT-based models and reduced their computational requirements. However, there has been limited research on combining KD and efficient attention architectures.
In this study, the researchers focus on combining KD and efficient attention architectures. They evaluate the performance of compressed efficient attention models using knowledge distillation on various tasks, including GLUE, SQuAD, HotpotQA, TriviaQA, CoNLL-2003, and GONERD. The results demonstrate that the distilled efficient attention models can preserve a significant amount of the original model's performance while reducing inference times by up to 57.8%.
The researchers introduce GONERD, a new long-context NER dataset, to address the need for a benchmark in long-context NER. They evaluate the performance of NER models on both CoNLL-2003 and GONERD datasets and find that performing knowledge distillation prior to fine-tuning on NER preserves 97.4% of CoNLL-2003 performance and improves GONERD performance.
In conclusion, this study shows that knowledge distillation is an effective method for creating high-performing efficient attention models with low costs. It provides insights into the trade-offs and benefits of compressed efficient attention models and highlights the importance of combining KD and efficient attention architectures. The introduction of GONERD dataset fills a gap in long-context NER benchmarking. The researchers have released all models on the Hugging Face Hub for general use. Further research is needed to explore other distillation methods tailored for individual efficient attention mechanisms, tasks, and architectures.
484 word summary
This study focuses on the intersection of model compression and efficient attention mechanisms in pretrained transformer language models. The researchers evaluate the performance of model compression via knowledge distillation on efficient attention transformers. They introduce a new long-context Named Entity Recognition (NER) dataset called GONERD and analyze the performance of NER models on long sequences. The results show that distilled efficient attention transformers can preserve a significant amount of the original model's performance while reducing inference times. The study demonstrates that knowledge distillation is an effective method for creating high-performing efficient attention models with low costs.
Transformer-based models, such as BERT and RoBERTa, have achieved state-of-the-art performance in Natural Language Processing (NLP) tasks. However, these models have limitations in processing long-context sequences due to their short maximum input length of 512 tokens. Efficient attention transformer models, such as Longformer, Big Bird, Nystromformer, and LSG, have been developed to address this limitation by accepting longer sequences with reduced computational overhead.
While efficient attention models require less computational resources than their non-efficient counterparts, they are still computationally expensive to train and deploy. This leads to increased operational costs and difficulty deploying the models on resource-limited hardware or in scenarios with limited internet access. In response to these challenges, the NLP community has been exploring cheaper yet performant models, such as those created through Knowledge Distillation (KD).
Knowledge Distillation is a technique that involves training a larger, complex model (teacher model) and distilling its knowledge into a smaller, simpler model (student model). This process has been successful in compressing BERT-based models and reducing their computational requirements. However, little work has been done on investigating the combination of KD and efficient attention architectures.
In this study, the researchers focus on combining KD and efficient attention architectures. They evaluate the performance of compressed efficient attention models using knowledge distillation on various tasks, including GLUE, SQuAD, HotpotQA, TriviaQA, CoNLL-2003, and GONERD. The results show that the distilled efficient attention models can preserve a significant amount of the original model's performance while reducing inference times by up to 57.8%.
The researchers also introduce GONERD, a new long-context NER dataset, to address the need for a benchmark in long-context NER. They evaluate the performance of NER models on both CoNLL-2003 and GONERD datasets and find that performing knowledge distillation prior to fine-tuning on NER preserves 97.4% of CoNLL-2003 performance and improves GONERD performance.
In conclusion, this study demonstrates that knowledge distillation is an effective method for creating high-performing efficient attention models with low costs. It provides insights into the performance trade-offs and benefits of compressed efficient attention models and highlights the importance of combining KD and efficient attention architectures. The introduction of GONERD dataset fills a gap in long-context NER benchmarking. The researchers release all models on the Hugging Face Hub for general use. Further research is needed to explore other distillation methods tailored for individual efficient attention mechanisms, tasks, and architectures.