Call for Papers

Important Dates

The submission deadline is extended to June 6!

• Submission Begins: May 20, 2024
  • Submission Portal: OpenReview
  • Template: Overleaf
• Submission Deadline: May 31, 2024 June 6, 2024 (11:59pm, anywhere on earth)
• Notification of Acceptance: June 17, 2024
• Camera-ready papers due: TBD
• Workshop Date: July 26, 2024

Topics of Interest

Many challenging tasks for foundation models require synthesizing information over thousands to millions of individual pieces of data, which may take many forms, including images, text, audio, genomes, etc. Our workshop aims to convene researchers to address challenges in long-context foundation models, fostering discussions, developments, deployments, evaluation, and understanding of long-context foundation models across various AI disciplines. The topics of this workshop include (but not limited to):

• New modeling, training, and data strategies.
• Efficiency techniques for (long-context) foundation models.
• Evaluation and understanding of long-context models.
• Retrieval-augmented foundation models.
• Interdisciplinary applications of LCFMs.

Guidelines

• Paper submission is hosted on OpenReview.
• We welcome papers up to 4 pages (max), not including references or appendix.
  • Please use the provided LaTex template (Overleaf) for your submission.
  • The paper should be anonymized and uploaded to OpenReview as a single PDF.
  • You may use as many pages of references and appendix as you wish, but reviewers are not required to read the appendix.
  • Posting papers on preprint servers like ArXiv is permitted.
• This is a non-archival workshop. No submission will be indexed nor have archival proceedings.
  • Accepted papers will appear on the workshop website. They will also be available on OpenReview and ICML virtual site.
  • We accept submissions that are under review at other venues (e.g., NeurIPS 2024), as long as this does not violate the dual-submission / anonymity policy of the other venue.
• The review process will be double-blind.
  

Organizers

Tianyu Gao

Princeton University

Weijia Shi
University of Washington

Amanda Bertsch
Carnegie Mellon University

Tri Dao
Together.AI., Princeton University

Danqi Chen
Princeton University

Graham Neubig
Carnegie Mellon University

Christopher Ré
Stanford University