Methods › Natural Language Processing › Language Models › BLOOM

BLOOM

116 papers tagged archive 2025-07-28

Introduced by BigScience Workshop et al. in BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

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

BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total).

PaperSource

Papers archive 2025-07-28

30 shown of 116, 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 140 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
Language Modeling12
Machine Translation8
Question Answering8
Text Generation8
Large Language Model6
Translation6
Quantization5
model5
Benchmarking4
Decoder4
Retrieval4
Cross-Lingual Transfer3
Diversity3
GPU3
Instruction Following3
MMLU3
Math3
Named Entity Recognition3
Sentence3

Usage over time archive 2025-07-28

Papers per year tagged with BLOOM: 2022 to 2025, peak 53 53 0 2022: 11 papers 2022 2023: 53 papers 2023 2024: 45 papers 2024 2025: 7 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (116 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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