Methods › General › Parameter Norm Penalties › ROME

Rank-One Model Editing

ROME

23 papers tagged archive 2025-07-28

Introduced by Kevin Meng et al. in Locating and Editing Factual Associations in GPT

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

The archive carries no description for this method.

PaperSource

Papers archive 2025-07-28

23 shown of 23, 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 31 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
Model Editing9
Language Modeling4
Language Modelling4
Specificity3
knowledge editing3
Large Language Model2
Memorization2
model2
Autonomous Driving1
Common Sense Reasoning1
Computational Efficiency1
Data Augmentation1
Density Estimation1
GPU1
In-Context Learning1
Math1
Mixture-of-Experts1
Natural Language Understanding1
Relation1
Relation Extraction1

Usage over time archive 2025-07-28

Papers per year tagged with ROME: 2022 to 2025, peak 9 9 0 2022: 4 papers 2022 2023: 6 papers 2023 2024: 9 papers 2024 2025: 4 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (23 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

Parameter Norm Penalties

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections