https://arxiv.org/abs/2307.02486

Skip to main content
Cornell University
We are hiring

We gratefully acknowledge support from the Simons Foundation, member
institutions, and all contributors. Donate
 
arxiv logo > cs > arXiv:2307.02486
[                    ]

Help | Advanced Search

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

  * Login
  * Help Pages
  * About

Computer Science > Computation and Language

arXiv:2307.02486 (cs)
[Submitted on 5 Jul 2023]

Title:LongNet: Scaling Transformers to 1,000,000,000 Tokens

Authors:Jiayu Ding, Shuming Ma, Li Dong, Xingxing Zhang, Shaohan
Huang, Wenhui Wang, Furu Wei
Download a PDF of the paper titled LongNet: Scaling Transformers to
1,000,000,000 Tokens, by Jiayu Ding and 6 other authors
Download PDF

    Abstract: Scaling sequence length has become a critical demand in
    the era of large language models. However, existing methods
    struggle with either computational complexity or model
    expressivity, rendering the maximum sequence length restricted.
    In this work, we introduce LongNet, a Transformer variant that
    can scale sequence length to more than 1 billion tokens, without
    sacrificing the performance on shorter sequences. Specifically,
    we propose dilated attention, which expands the attentive field
    exponentially as the distance grows. LongNet has significant
    advantages: 1) it has a linear computation complexity and a
    logarithm dependency between tokens; 2) it can be served as a
    distributed trainer for extremely long sequences; 3) its dilated
    attention is a drop-in replacement for standard attention, which
    can be seamlessly integrated with the existing Transformer-based
    optimization. Experiments results demonstrate that LongNet yields
    strong performance on both long-sequence modeling and general
    language tasks. Our work opens up new possibilities for modeling
    very long sequences, e.g., treating a whole corpus or even the
    entire Internet as a sequence.

Comments: Work in progress
Subjects: Computation and Language (cs.CL); Machine Learning (cs.LG)
Cite as:  arXiv:2307.02486 [cs.CL]
          (or arXiv:2307.02486v1 [cs.CL] for this version)
          https://doi.org/10.48550/arXiv.2307.02486
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Shuming Ma [view email]
[v1] Wed, 5 Jul 2023 17:59:38 UTC (219 KB)
Full-text links:

Download:

  * Download a PDF of the paper titled LongNet: Scaling Transformers
    to 1,000,000,000 Tokens, by Jiayu Ding and 6 other authors
    PDF
  * Other formats

(license)
Current browse context:
cs.CL
< prev   |   next >
new | recent | 2307
Change to browse by:
cs
cs.LG

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?)
( ) 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