https://arxiv.org/abs/2505.24832

Skip to main content
Cornell University
We gratefully acknowledge support from the Simons Foundation, member
institutions, and all contributors. Donate
 
arxiv logo > cs > arXiv:2505.24832
[                    ]

Help | Advanced Search

[All fields        ]
Search
arXiv logo
Cornell University Logo
[                    ] GO
quick links

  * Login
  * Help Pages
  * About

Computer Science > Computation and Language

arXiv:2505.24832 (cs)
[Submitted on 30 May 2025 (v1), last revised 2 Jun 2025 (this
version, v2)]

Title:How much do language models memorize?

Authors:John X. Morris, Chawin Sitawarin, Chuan Guo, Narine
Kokhlikyan, G. Edward Suh, Alexander M. Rush, Kamalika Chaudhuri,
Saeed Mahloujifar
View a PDF of the paper titled How much do language models memorize?,
by John X. Morris and 7 other authors
View PDF HTML (experimental)

    Abstract:We propose a new method for estimating how much a model
    ``knows'' about a datapoint and use it to measure the capacity of
    modern language models. Prior studies of language model
    memorization have struggled to disentangle memorization from
    generalization. We formally separate memorization into two
    components: \textit{unintended memorization}, the information a
    model contains about a specific dataset, and \textit
    {generalization}, the information a model contains about the true
    data-generation process. When we completely eliminate
    generalization, we can compute the total memorization, which
    provides an estimate of model capacity: our measurements estimate
    that GPT-style models have a capacity of approximately 3.6 bits
    per parameter. We train language models on datasets of increasing
    size and observe that models memorize until their capacity fills,
    at which point ``grokking'' begins, and unintended memorization
    decreases as models begin to generalize. We train hundreds of
    transformer language models ranging from $500K$ to $1.5B$
    parameters and produce a series of scaling laws relating model
    capacity and data size to membership inference.

Subjects: Computation and Language (cs.CL)
Cite as:  arXiv:2505.24832 [cs.CL]
          (or arXiv:2505.24832v2 [cs.CL] for this version)
          https://doi.org/10.48550/arXiv.2505.24832
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: John Morris [view email]
[v1] Fri, 30 May 2025 17:34:03 UTC (6,686 KB)
[v2] Mon, 2 Jun 2025 14:13:41 UTC (3,282 KB)
Full-text links:

Access Paper:

    View a PDF of the paper titled How much do language models
    memorize?, by John X. Morris and 7 other authors
  * View PDF
  * HTML (experimental)
  * TeX Source
  * Other Formats

license icon view license
Current browse context:
cs.CL
< prev   |   next >
new | recent | 2025-05
Change to browse by:
cs

References & Citations

  * NASA ADS
  * Google Scholar
  * Semantic Scholar

a export BibTeX citation Loading...

BibTeX formatted citation

x
[loading...          ]
Data provided by:

Bookmark

BibSonomy logo Reddit logo
(*) Bibliographic Tools

Bibliographic and Citation Tools

[ ] Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
[ ] Connected Papers Toggle
Connected Papers (What is Connected Papers?)
[ ] Litmaps Toggle
Litmaps (What is Litmaps?)
[ ] scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
( ) Code, Data, Media

Code, Data and Media Associated with this Article

[ ] alphaXiv Toggle
alphaXiv (What is alphaXiv?)
[ ] Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
[ ] DagsHub Toggle
DagsHub (What is DagsHub?)
[ ] GotitPub Toggle
Gotit.pub (What is GotitPub?)
[ ] Huggingface Toggle
Hugging Face (What is Huggingface?)
[ ] Links to Code Toggle
Papers with Code (What is Papers with Code?)
[ ] ScienceCast Toggle
ScienceCast (What is ScienceCast?)
( ) Demos

Demos

[ ] Replicate Toggle
Replicate (What is Replicate?)
[ ] Spaces Toggle
Hugging Face Spaces (What is Spaces?)
[ ] Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
( ) Related Papers

Recommenders and Search Tools

[ ] Link to Influence Flower
Influence Flower (What are Influence Flowers?)
[ ] Core recommender toggle
CORE Recommender (What is CORE?)

  * Author
  * Venue
  * Institution
  * Topic

( ) About arXivLabs

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and
share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have
embraced and accepted our values of openness, community, excellence,
and user data privacy. arXiv is committed to these values and only
works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community?
Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is
MathJax?)

  * About
  * Help

  * Click here to contact arXiv Contact
  * Click here to subscribe Subscribe

  * Copyright
  * Privacy Policy

  * Web Accessibility Assistance
  * arXiv Operational Status
    Get status notifications via email or slack