Methods › Natural Language Processing › Autoencoding Transformers › XLM

XLM

57 papers tagged archive 2025-07-28

Introduced by Guillaume Lample et al. in Cross-lingual Language Model Pretraining

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

XLM is a Transformer based architecture that is pre-trained using one of three language modelling objectives:

  1. Causal Language Modeling - models the probability of a word given the previous words in a sentence.
  2. Masked Language Modeling - the masked language modeling objective of BERT.
  3. Translation Language Modeling - a (new) translation language modeling objective for improving cross-lingual pre-training.

The authors find that both the CLM and MLM approaches provide strong cross-lingual features that can be used for pretraining models.

PaperSource

Papers archive 2025-07-28

30 shown of 57, 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 106 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 Modelling17
Translation15
Language Modeling14
Sentence11
Machine Translation10
Cross-Lingual Transfer8
Question Answering8
XLM-R8
Transfer Learning7
NER6
Retrieval6
NMT5
Natural Language Understanding5
Text Classification5
Word Embeddings5
Classification4
Natural Language Inference4
Representation Learning4
Zero-Shot Cross-Lingual Transfer4
text-classification4

Usage over time archive 2025-07-28

Papers per year tagged with XLM: 2019 to 2024, peak 16 16 0 2019: 9 papers 2019 2020: 15 papers 2020 2021: 16 papers 2021 2022: 7 papers 2022 2023: 6 papers 2023 2024: 4 papers 2024
Papers per year the archive tags with this method, by the paper's archive date (57 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

Autoencoding TransformersTransformers

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