Methods › Natural Language Processing › Language Models › GLM

GLM

46 papers tagged archive 2025-07-28

Introduced by Aohan Zeng et al. in GLM-130B: An Open Bilingual Pre-trained Model

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

GLM is a bilingual (English and Chinese) pre-trained transformer-based language model that follow the traditional architecture of decoder-only autoregressive language modeling. It leverages autoregressive blank infilling as its training objective.

PaperSource

Papers archive 2025-07-28

30 shown of 46, 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 63 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
regression7
Language Modelling6
Language Modeling5
Quantization3
Denoising2
Diversity2
Large Language Model2
Question Answering2
Retrieval2
Retrieval-augmented Generation2
Semantic Segmentation2
model2
parameter estimation2
parameter-efficient fine-tuning2
Adversarial Robustness1
Causal Discovery1
Chatbot1
Classification1
Conformal Prediction1
Data Augmentation1

Usage over time archive 2025-07-28

Papers per year tagged with GLM: 2022 to 2025, peak 25 25 0 2022: 7 papers 2022 2023: 25 papers 2023 2024: 8 papers 2024 2025: 6 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (46 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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