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Paper

Mass-Editing Memory in a Transformer

by Kevin Meng, Arnab Sen Sharma, A. Andonian, Yonatan Belinkov, David Bau 2210.07229

Free2AITools Nexus Index

61.9

S: Semantic 50

Query-time baseline · scored live at search

A: Authority 92

P: Popularity 72

R: Recency 100

Q: Quality 65

Tech Context

Vital Performance —

Recent work has shown exciting promise in updating large language models with new memories, so as to replace obsolete information or add specialized knowledge. However, this line of work is predominantly limited to updating single associations. We develop MEMIT, a method for directly updating a language model with many memories, demonstrating experimentally that it can scale up to thousands of associations for GPT-J (6B) and GPT-NeoX (20B), exceeding prior work by orders of magnitude. Our cod...

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Semantic Scholar 821 Citations

Paper Information Summary
Entity Passport
Registry ID	2210.07229
License	ArXiv
Provider	semantic_scholar

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Cite this paper

Academic & Research Attribution

BibTeX

@misc{arxiv_2210_07229,
  author = {Kevin Meng, Arnab Sen Sharma, A. Andonian, Yonatan Belinkov, David Bau},
  title = {Mass-Editing Memory in a Transformer Paper},
  year = {2026},
  howpublished = {\url{https://arxiv.org/abs/2210.07229}},
  note = {Accessed via Free2AITools.}
}

APA Style

Kevin Meng, Arnab Sen Sharma, A. Andonian, Yonatan Belinkov, David Bau. (2026). Mass-Editing Memory in a Transformer [Paper]. Free2AITools. https://arxiv.org/abs/2210.07229

🔬Technical Deep Dive

Full Specifications [+]

⚖️ Free2AITools Nexus Index V2.0

Methodology How FNI works

Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 92

Popularity (P) 72

Recency (R) 100

Quality (Q) 65

💬 Index Insight

FNI V2.0 for Mass-Editing Memory in a Transformer: Authority (A:92), Popularity (P:72), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

Free2AITools Nexus Index

Data Sources / Provenance

HuggingFace API GitHub Metadata Arxiv Citation DB Methodology

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📝 Executive Summary

"Recent work has shown exciting promise in updating large language models with new memories, so as to replace obsolete information or add specialized knowledge. However, this line of work is predominantly limited to updating single associations. We develop MEMIT, a method for directly updating a language model with many memories, demonstrating experimentally that it can scale up to thousands of associations for GPT-J (6B) and GPT-NeoX (20B), exceeding prior work by orders of magnitude. Our cod..."

❝ Cite Node

@article{Meng2026Mass-Editing,
  title={Mass-Editing Memory in a Transformer},
  author={Kevin Meng and Arnab Sen Sharma and A. Andonian and Yonatan Belinkov and David Bau},
  journal={arXiv preprint arXiv:2210.07229},
  year={2026}
}

👥 Collaborating Minds

Kevin Meng Arnab Sen Sharma A. Andonian Yonatan Belinkov David Bau

🔗 Full Paper

Free2AITools indexes the abstract and factual metadata for this paper. Read the complete, authoritative paper on the official source.

Read the full paper on arXiv

📊 Research Signals

📈821CitationsSemantic Scholar

🏛️92AuthorityFNI pillar

⏱️100RecencyFNI pillar

✅65QualityFNI pillar

🗂️text generationField

📦Data Source: semantic_scholar

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Source summary: Based on semantic_scholar metadata. Not a recommendation.

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🛡️ Paper Transparency Report

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🆔 Identity & Source

id: 2210.07229
slug: 2210.07229
source: semantic_scholar
author: Kevin Meng, Arnab Sen Sharma, A. Andonian, Yonatan Belinkov, David Bau
license: ArXiv
tags: paper, research, academic

⚙️ Technical Specs

architecture: null
params billions: null
context length: null
pipeline tag

📊 Engagement & Metrics

downloads: 0
stars: 0
forks: 0
citations: 821

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