The NLP Task Effectiveness of Long-Range Transformers

By Guanghui Qin, Yukun Feng, Benjamin Van Durme. 2022

42 citations recorded by the source corpus.

Transformer models cannot easily scale to long sequences due to their O(Nˆ2) time and space complexity. This has led to Transformer variants seeking to lower computational complexity, such as Longformer and Performer. While such models have theoretically greater efficiency, their effectiveness on real NLP tasks has not been well studied. We benchmark 7 variants of Transformer models on 5 difficult NLP tasks and 7 datasets. We design experiments to isolate the effect of pretraining and hyperparameter settings, to focus on their capacity for long-range attention. Moreover, we present various methods to investigate attention behaviors to illuminate model details beyond metric scores. We find that the modified attention in long-range transformers has 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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Natural Language Processing, Model Architectures, Efficiency & Compression

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Citations in the corpus

Lost in the Middle: How Language Models Use Long Contexts (2023, background): “Qin et al. (2023) analyze how efficient Transformers perform on a variety of long-context downstream NLP tasks, finding that long-context transformers are recency-biased and do not effectively use long-range context.”