https://arxiv.org/abs/2401.17377

Skip to main content
Cornell University
We gratefully acknowledge support from the Simons Foundation, member
institutions, and all contributors. Donate
 
arxiv logo > cs > arXiv:2401.17377
[                    ]

Help | Advanced Search

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

  * Login
  * Help Pages
  * About

Computer Science > Computation and Language

arXiv:2401.17377 (cs)
[Submitted on 30 Jan 2024 (v1), last revised 4 Apr 2024 (this
version, v3)]

Title:Infini-gram: Scaling Unbounded n-gram Language Models to a
Trillion Tokens

Authors:Jiacheng Liu, Sewon Min, Luke Zettlemoyer, Yejin Choi,
Hannaneh Hajishirzi
View a PDF of the paper titled Infini-gram: Scaling Unbounded n-gram
Language Models to a Trillion Tokens, by Jiacheng Liu and 4 other
authors
View PDF HTML (experimental)

    Abstract:Are $n$-gram language models still relevant in this era
    of neural large language models (LLMs)? Our answer is yes, and we
    showcase their values in both text analysis and improving neural
    LLMs. This was done by modernizing $n$-gram LMs in two aspects.
    First, we train them at the same data scale as neural LLMs -- 5
    trillion tokens. This is the largest $n$-gram LM ever built.
    Second, existing $n$-gram LMs use small $n$ which hinders their
    performance; we instead allow $n$ to be arbitrarily large, by
    introducing a new $\infty$-gram LM with backoff. Instead of
    pre-computing $n$-gram count tables (which would be very
    expensive), we develop an engine named infini-gram -- powered by
    suffix arrays -- that can compute $\infty$-gram (as well as
    $n$-gram with arbitrary $n$) probabilities with millisecond-level
    latency. The $\infty$-gram framework and infini-gram engine
    enable us to conduct many novel and interesting analyses of
    human-written and machine-generated text: we find that the $\
    infty$-gram LM has fairly high accuracy for next-token prediction
    (47%), and can complement neural LLMs to greatly reduce their
    perplexity. When analyzing machine-generated text, we also
    observe irregularities in the machine--$\infty$-gram agreement
    level with respect to the suffix length, which indicates
    deficiencies in neural LLM pretraining and the positional
    embeddings of Transformers.

Subjects: Computation and Language (cs.CL); Artificial Intelligence
          (cs.AI); Information Retrieval (cs.IR)
Cite as:  arXiv:2401.17377 [cs.CL]
          (or arXiv:2401.17377v3 [cs.CL] for this version)
          https://doi.org/10.48550/arXiv.2401.17377
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Jiacheng Liu [view email]
[v1] Tue, 30 Jan 2024 19:03:49 UTC (6,464 KB)
[v2] Tue, 2 Apr 2024 18:14:53 UTC (7,819 KB)
[v3] Thu, 4 Apr 2024 17:28:38 UTC (7,917 KB)
Full-text links:

Access Paper:

    View a PDF of the paper titled Infini-gram: Scaling Unbounded
    n-gram Language Models to a Trillion Tokens, by Jiacheng Liu and
    4 other authors
  * View PDF
  * HTML (experimental)
  * TeX Source
  * Other Formats

license icon view license
Current browse context:
cs.CL
< prev   |   next >
new | recent | 2401
Change to browse by:
cs
cs.AI
cs.IR

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?)
[ ] GotitPub Toggle
Gotit.pub (What is GotitPub?)
[ ] 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