https://arxiv.org/abs/2312.13401

Skip to main content
Cornell University
Served from the cloud
We gratefully acknowledge support from the Simons Foundation, member
institutions, and all contributors. Donate
 
arxiv logo > cs > arXiv:2312.13401
[                    ]

Help | Advanced Search

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

  * Login
  * Help Pages
  * About

Computer Science > Computation and Language

arXiv:2312.13401 (cs)
[Submitted on 20 Dec 2023]

Title:Time is Encoded in the Weights of Finetuned Language Models

Authors:Kai Nylund, Suchin Gururangan, Noah A. Smith
Download a PDF of the paper titled Time is Encoded in the Weights of
Finetuned Language Models, by Kai Nylund and 2 other authors
Download PDF HTML (experimental)

    Abstract:We present time vectors, a simple tool to customize
    language models to new time periods. Time vectors are created by
    finetuning a language model on data from a single time (e.g., a
    year or month), and then subtracting the weights of the original
    pretrained model. This vector specifies a direction in weight
    space that, as our experiments show, improves performance on text
    from that time period. Time vectors specialized to adjacent time
    periods appear to be positioned closer together in a manifold.
    Using this structure, we interpolate between time vectors to
    induce new models that perform better on intervening and future
    time periods, without any additional training. We demonstrate the
    consistency of our findings across different tasks, domains,
    model sizes, and time scales. Our results suggest that time is
    encoded in the weight space of finetuned models.

Subjects: Computation and Language (cs.CL)
Cite as:  arXiv:2312.13401 [cs.CL]
          (or arXiv:2312.13401v1 [cs.CL] for this version)
          https://doi.org/10.48550/arXiv.2312.13401
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Kai Nylund [view email]
[v1] Wed, 20 Dec 2023 20:04:45 UTC (8,942 KB)
Full-text links:

Access Paper:

    Download a PDF of the paper titled Time is Encoded in the Weights
    of Finetuned Language Models, by Kai Nylund and 2 other authors
  * Download PDF
  * PostScript
  * Other Formats
  * HTML (experimental)

(view license)
Current browse context:
cs.CL
< prev   |   next >
new | recent | 2312
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?)
[ ] 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

[ ] Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
[ ] DagsHub Toggle
DagsHub (What is DagsHub?)
[ ] 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?)
[ ] Connected Papers Toggle
Connected Papers (What is Connected Papers?)
[ ] 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