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<p><b>CFP - AAAI-2025 Workshop on Preparing Good Data for Generative
AI: Challenges and Approaches (GoodData)</b><br>
<br>
For details, please see the webpage:
<a class="moz-txt-link-freetext" href="https://sites.google.com/servicenow.com/good-data-2025/">https://sites.google.com/servicenow.com/good-data-2025/</a><br>
<br>
Foundation models highly depend on the data they are trained on.
Although self-supervised learning is one of their promises, it is
clear that the carefully processed datasets lead to better models.
While datasets and models are frequently released by the
community, the data preparation recipes are relatively nascent and
not fully open. In this workshop, we invite contributions and
collaborations in data preparation recipes for creating and using
foundation models and generative AI applications, including (but
not limited to) pre-training, alignment, fine tuning, and
in-context learning. Data preparation spans data acquisition,
cleaning, processing, mixtures, quality assessments, value of
data, ablation studies, safety, and governance. This workshop
emphasizes the responsible usage and ethical considerations of
data preparation (including human annotations), to address the
issues of diversity, bias, transparency, and privacy.<br>
<br>
<b>---Important Dates---</b><br>
Workshop paper submission deadline: 15 November 2024, 11:59 pm
Pacific Time.<br>
Notification to authors: 9 December 2024.<br>
Date of workshop: 3 or 4 March 2025.<br>
<br>
<b>---Topics---</b><br>
We encourage submissions under one of these topics of interest,
but we also welcome other interesting and relevant research for
preparing good data.<br>
Data acquisition, cleaning, processing, and mixture recipes<br>
Data quality assessment and quantifying the value of data<br>
Data sequence for multi-phase and curriculum learning<br>
Model-based data improvement techniques<br>
Ablation study strategies to understand the interplay between data
and model<br>
Data safety and governance<br>
Responsible and ethical considerations of data collection and
human annotation<br>
Diversity, bias, transparency, and privacy of data<br>
Theoretical modeling and analysis of data-related aspects in
generative AI<br>
Large-scale data processing (intersection between systems and
algorithms)<br>
Data value<br>
<br>
We accept submissions of a maximum of 4 pages (excluding
references and appendix). Papers will be peer-reviewed under a
double-blind policy. Accepted papers will be presented at the
poster session, some as oral presentations, and one paper will be
awarded as the best paper.<br>
<br>
<br>
<b>---OpenReview Submission Link---</b><br>
Please submit your paper via the following link:
<a class="moz-txt-link-freetext" href="https://openreview.net/group?id=AAAI.org/2025/Workshop/GoodData">https://openreview.net/group?id=AAAI.org/2025/Workshop/GoodData</a> <br>
<br>
<b>---Submission Guidelines---</b><br>
We accept submissions of a maximum of 4 pages (excluding
references and appendix).<br>
We accept only original works not published before at any archival
venue with proceedings.<br>
The submitted manuscript should follow the AAAI 2025 paper
template.<br>
Submissions will be rejected without review if they:<br>
Contain more than 4 pages (excluding references and appendix).<br>
Violate the double-blind policy.<br>
Violate the dual-submission policy for papers.<br>
The accepted papers will be publicly accessible on OpenReview, but
the workshop is non-archival and does not have formal proceedings.<br>
Papers will be peer-reviewed under a double-blind policy and must
be submitted online through the OpenReview submission system.<br>
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<pre class="moz-signature" cols="72">--
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Laure Berti-Equille
IRD ESPACE-DEV
Institut de Recherche pour le Développement
<a class="moz-txt-link-freetext" href="https://laureberti.github.io/website/">https://laureberti.github.io/website/</a></pre>
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