Methods › Natural Language Processing › Language Models › UL2

UL2

8 papers tagged archive 2025-07-28

Introduced by Yi Tay et al. in UL2: Unifying Language Learning Paradigms

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

UL2 is a unified framework for pretraining models that are universally effective across datasets and setups. UL2 uses Mixture-of-Denoisers (MoD), a pre-training objective that combines diverse pre-training paradigms together. UL2 introduces a notion of mode switching, wherein downstream fine-tuning is associated with specific pre-training schemes.

PaperSource

Papers archive 2025-07-28

8 shown of 8, 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 30 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
Question Answering5
Retrieval3
Language Modeling2
Language Modelling2
Natural Language Inference2
Relation Extraction2
Arithmetic Reasoning1
Attribute1
Common Sense Reasoning1
Coreference Resolution1
Decoder1
Extractive Question-Answering1
In-Context Learning1
Inductive Bias1
Information Retrieval1
Knowledge Base Construction1
Long-range modeling1
MMLU1
Multi-task Language Understanding1
NER1

Usage over time archive 2025-07-28

Papers per year tagged with UL2: 2022 to 2024, peak 3 3 0 2022: 3 papers 2022 2023: 3 papers 2023 2024: 2 papers 2024
Papers per year the archive tags with this method, by the paper's archive date (8 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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