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Probabilistically Masked Language Model

PMLM

3 papers tagged archive 2025-07-28

Introduced by Yi Liao et al. in Probabilistically Masked Language Model Capable of Autoregressive Generation in Arbitrary Word Order

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

Probabilistically Masked Language Model, or PMLM, is a type of language model that utilizes a probabilistic masking scheme, aiming to bridge the gap between masked and autoregressive language models. The basic idea behind the connection of two categories of models is similar to MADE by Germain et al (2015). PMLM is a masked language model with a probabilistic masking scheme, which defines the way sequences are masked by following a probabilistic distribution. The authors employ a simple uniform distribution of the masking ratio and name the model as u-PMLM.

Paper Source

Papers archive 2025-07-28

3 shown of 3, 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.

From General to Specific: Tailoring Large Language Models for Personalized Healthcare
20 Dec 2024 · 0 repositories · arXiv:2412.15957
Pre-training Co-evolutionary Protein Representation via A Pairwise Masked Language Model
29 Oct 2021 · 0 repositories · arXiv:2110.15527
Probabilistically Masked Language Model Capable of Autoregressive Generation in Arbitrary Word Order
24 Apr 2020 · 3 repositories · arXiv:2004.11579

Syntology ran 18 of 35 samples · 17 unverified · 29 pointer-only (licence)

Tasks archive 2025-07-28

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

Task	Papers
Language Modeling	3
Language Modelling	3
Multiple Sequence Alignment	1
Natural Language Understanding	1
Recommendation Systems	1
Reinforcement Learning (RL)	1
Representation Learning	1
Text Generation	1

Usage over time archive 2025-07-28

Papers per year the archive tags with this method, by the paper's archive date (3 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

Language Models

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