Methods › Natural Language Processing › Transformers › XLNet

XLNet

167 papers tagged archive 2025-07-28

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

XLNet is an autoregressive Transformer that leverages the best of both autoregressive language modeling and autoencoding while attempting to avoid their limitations. Instead of using a fixed forward or backward factorization order as in conventional autoregressive models, XLNet maximizes the expected log likelihood of a sequence w.r.t. all possible permutations of the factorization order. Thanks to the permutation operation, the context for each position can consist of tokens from both left and right. In expectation, each position learns to utilize contextual information from all positions, i.e., capturing bidirectional context.

Additionally, inspired by the latest advancements in autogressive language modeling, XLNet integrates the segment recurrence mechanism and relative encoding scheme of Transformer-XL into pretraining, which empirically improves the performance especially for tasks involving a longer text sequence.

Source: XLNet: Generalized Autoregressive Pretraining for...

Papers archive 2025-07-28

30 shown of 167, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

20 shown of 190 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Language Modelling23
Language Modeling22
Sentence20
Question Answering17
Sentiment Analysis17
Transfer Learning14
Text Classification13
Reading Comprehension11
text-classification10
Data Augmentation9
General Classification8
Natural Language Understanding8
Text Generation8
Named Entity Recognition (NER)7
Representation Learning7
Classification6
Named Entity Recognition6
Sentiment Classification6
Translation6
Word Embeddings6

Usage over time archive 2025-07-28

Papers per year tagged with XLNet: 2019 to 2025, peak 58 58 0 2019: 25 papers 2019 2020: 58 papers 2020 2021: 42 papers 2021 2022: 14 papers 2022 2023: 14 papers 2023 2024: 11 papers 2024 2025: 3 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (167 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Transformers

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