| Challenge: | Existing benchmarks on long-range attention models have not been sufficient to develop efficient Transformers and their practical application on complex NLP tasks. |
| Approach: | They propose to benchmark 7 Transformer variants on 5 difficult NLP tasks and 7 datasets to examine their capacity for long-range attention. |
| Outcome: | The proposed models have advantages on content selection and query-guided decoding, but they come with previously unrecognized drawbacks such as insufficient attention to distant tokens and accumulated approximation error. |
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| Challenge: | Pretrained transformer models have demonstrated remarkable performance across various natural language processing tasks. |
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On the Distribution, Sparsity, and Inference-time Quantization of Attention Values in Transformers (2021.findings-acl)
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Tianchu Ji, Shraddhan Jain, Michael Ferdman, Peter Milder, H. Andrew Schwartz, Niranjan Balasubramanian
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Joshua Ainslie, Santiago Ontanon, Chris Alberti, Vaclav Cvicek, Zachary Fisher, Philip Pham, Anirudh Ravula, Sumit Sanghai, Qifan Wang, Li Yang
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Compressing Large-Scale Transformer-Based Models: A Case Study on BERT (2021.tacl-1)
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A Primer in BERTology: What We Know About How BERT Works (2020.tacl-1)
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Long-Span Summarization via Local Attention and Content Selection (2021.acl-long)
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| Challenge: | Transformer-based models are state-of-the-art for a wide range of natural language processing tasks, including document summarization. |
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Adaptive Attention for Sparse-based Long-sequence Transformer (2023.findings-acl)
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| Challenge: | Recent studies show that Transformers can process longer sequences because of their complexity and time scales quadratic to the sequence length. |
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