https://arxiv.org/abs/2306.05426

Skip to main content
Cornell University
We are hiring

We gratefully acknowledge support from
the Simons Foundation and member institutions.
 
arxiv logo > cs > arXiv:2306.05426
[                    ]

Help | Advanced Search

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

  * Login
  * Help Pages
  * About

Computer Science > Machine Learning

arXiv:2306.05426 (cs)
[Submitted on 8 Jun 2023 (v1), last revised 19 Jun 2023 (this
version, v2)]

Title:SequenceMatch: Imitation Learning for Autoregressive Sequence
Modelling with Backtracking

Authors:Chris Cundy, Stefano Ermon
Download a PDF of the paper titled SequenceMatch: Imitation Learning
for Autoregressive Sequence Modelling with Backtracking, by Chris
Cundy and 1 other authors
Download PDF

    Abstract: In many domains, autoregressive models can attain high
    likelihood on the task of predicting the next observation.
    However, this maximum-likelihood (MLE) objective does not
    necessarily match a downstream use-case of autoregressively
    generating high-quality sequences. The MLE objective weights
    sequences proportionally to their frequency under the data
    distribution, with no guidance for the model's behaviour out of
    distribution (OOD): leading to compounding error during
    autoregressive generation. In order to address this compounding
    error problem, we formulate sequence generation as an imitation
    learning (IL) problem. This allows us to minimize a variety of
    divergences between the distribution of sequences generated by an
    autoregressive model and sequences from a dataset, including
    divergences with weight on OOD generated sequences. The IL
    framework also allows us to incorporate backtracking by
    introducing a backspace action into the generation process. This
    further mitigates the compounding error problem by allowing the
    model to revert a sampled token if it takes the sequence OOD. Our
    resulting method, SequenceMatch, can be implemented without
    adversarial training or major architectural changes. We identify
    the SequenceMatch-$\chi^2$ divergence as a more suitable training
    objective for autoregressive models which are used for
    generation. We show that empirically, SequenceMatch training
    leads to improvements over MLE on text generation with language
    models.

Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as:  arXiv:2306.05426 [cs.LG]
          (or arXiv:2306.05426v2 [cs.LG] for this version)
          https://doi.org/10.48550/arXiv.2306.05426
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Chris Cundy [view email]
[v1] Thu, 8 Jun 2023 17:59:58 UTC (841 KB)
[v2] Mon, 19 Jun 2023 17:59:24 UTC (841 KB)
Full-text links:

Download:

  * Download a PDF of the paper titled SequenceMatch: Imitation
    Learning for Autoregressive Sequence Modelling with Backtracking,
    by Chris Cundy and 1 other authors
    PDF
  * Other formats

[by-sa-4]
Current browse context:
cs.LG
< prev   |   next >
new | recent | 2306
Change to browse by:
cs
cs.AI

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

[ ] 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?)
[ ] IArxiv recommender toggle
IArxiv Recommender (What is IArxiv?)

  * 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