*New paper submission and ARR commitment deadlines (see below)*
We invite you to participate and submit your work to the First Workshop
on Data Contamination (CONDA) co-located with ACL 2024 in Bangkok, Thailand.
Data contamination, where evaluation data is inadvertently included in
pre-training corpora of large scale models, and language models (LMs) in
particular, has become a concern in recent times. The growing scale of
both models and data, coupled with massive web crawling, has led to the
inclusion of segments from evaluation benchmarks in the pre-training
data of LMs. The scale of internet data makes it difficult to prevent
this contamination from happening, or even detect when it has happened.
Crucially, when evaluation data becomes part of pre-training data, it
introduces biases and can artificially inflate the performance of LMs on
specific tasks or benchmarks. This poses a challenge for fair and
unbiased evaluation of models, as their performance may not accurately
reflect their generalization capabilities.
Although a growing number of papers and state-of-the-art models mention
issues of data contamination, there is no agreed-upon definition or
standard methodology to ensure that a model does not report results on
contaminated benchmarks. Addressing data contamination is a shared
responsibility among researchers, developers, and the broader community.
By adopting best practices, increasing transparency, documenting
vulnerabilities, and conducting thorough evaluations, we can work
towards minimizing the impact of data contamination and ensuring fair
and reliable evaluations.
We welcome paper submissions on all topics related to data
contamination, including but not limited to:
* Definitions, taxonomies, and gradings of contamination
* Contamination detection (both manual and automatic)
* Community efforts to discover, report, and organize contamination events
* Documentation frameworks for datasets or models
* Methods to avoid data contamination
* Methods to forget contaminated data
* Scaling laws and contamination
* Memorization and contamination
* Policies to avoid impact of contamination in publication venues and
open source communities
* Reproducing and attributing results from previous work to data
contamination
* Survey work on data contamination research
* Data contamination in other modalities
*/
/*
*/Submission Instructions/*
We welcome two types of papers: regular workshop papers and non-archival
submissions. Regular workshop papers will be included in the workshop
proceedings. All submissions must be in PDF format and made through
OpenReview.
*
* *Regular workshop papers:*Authors can submit papers up to 8 pages,
with unlimited pages for references. Authors may submit up to 100 MB
of supplementary materials separately and their code for
reproducibility. All submissions undergo a double-blind single-track
review. Best Paper Award(s) will be given based on nomination by the
reviewers. Accepted papers will be presented as posters with the
possibility of oral presentations.
*
* *Non-archival submissions:*Cross-submissions are welcome. Accepted
papers will be presented at the workshop but not included in the
workshop proceedings. Papers must be in PDF format and will be
reviewed in a double-blind fashion by workshop reviewers. We also
welcome extended abstracts (up to 2 pages) of papers that are work
in progress, under review or to be submitted to other venues. Papers
in this category need to follow the ACL format.
*
In addition to papers submitted directly to the workshop, which will be
reviewed by our Programme Committee. We also accept papers reviewed
through ACL Rolling Review and committed to the workshop. Please, check
the relevant dates for each type of submission.
*/
/*
*/Important dates/*
Relevant deadlines to consider when submitting your paper are:
* *Paper submission deadline: May 31 (Friday), 2024*
* *ARR pre-reviewed commitment deadline: June 14 (Friday), 2024*
* Notification of acceptance: June 17 (Monday), 2024
* Camera-ready paper due: July 1 (Monday), 2024
* Workshop date: August 16, 2024
*/
/*
*/Sponsors/*
* AWS AI and Amazon Bedrock
* HuggingFace
* Google
*/
/*
*/Contact/*
* Website:https://conda-workshop.github.io/
<https://conda-workshop.github.io/>
* Email:[email protected]<mailto:[email protected]>
*/
/*
*/Organizers/*
Oscar Sainz, University of the Basque Country (UPV/EHU)
Iker García Ferrero, University of the Basque Country (UPV/EHU)
Eneko Agirre, University of the Basque Country (UPV/EHU)
Jon Ander Campos, Cohere
Alon Jacovi, Bar Ilan University
Yanai Elazar, Allen Institute for Artificial Intelligence and
University of Washington
Yoav Goldberg, Bar Ilan University and Allen Institute for Artificial
Intelligence
--
Eneko Agirre
HiTZ Hizkuntza Teknologiako Zentroa - Ixa Taldea
Centro Vasco de Tecnología de la Lengua - Grupo Ixa
Basque Center for Language Technology - Ixa NLP Group
University of the Basque Country (UPV/EHU)
hitz.ehu.eus/eneko <https://hitz.ehu.eus/eneko>
_______________________________________________
Corpora mailing list -- [email protected]
https://list.elra.info/mailman3/postorius/lists/corpora.list.elra.info/
To unsubscribe send an email to [email protected]
