Connectionists: CFP - AAAI-2025 Workshop on Preparing Good Data for Generative AI: Challenges and Approaches (GoodData)

[Laure Berti]

*CFP - AAAI-2025 Workshop on Preparing Good Data for Generative AI: 
Challenges and Approaches (GoodData)*

For details, please see the webpage: 
https://sites.google.com/servicenow.com/good-data-2025/

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.

*---Important Dates---*
Workshop paper submission deadline: 15 November 2024, 11:59 pm Pacific Time.
Notification to authors: 9 December 2024.
Date of workshop: 3 or 4 March 2025.

*---Topics---*
We encourage submissions under one of these topics of interest, but we 
also welcome other interesting and relevant research for preparing good 
data.
Data acquisition, cleaning, processing, and mixture recipes
Data quality assessment and quantifying the value of data
Data sequence for multi-phase and curriculum learning
Model-based data improvement techniques
Ablation study strategies to understand the interplay between data and model
Data safety and governance
Responsible and ethical considerations of data collection and human 
annotation
Diversity, bias, transparency, and privacy of data
Theoretical modeling and analysis of data-related aspects in generative AI
Large-scale data processing (intersection between systems and algorithms)
Data value

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.


*---OpenReview Submission Link---*
Please submit your paper via the following link: 
https://openreview.net/group?id=AAAI.org/2025/Workshop/GoodData

*---Submission Guidelines---*
We accept submissions of a maximum of 4 pages (excluding references and 
appendix).
We accept only original works not published before at any archival venue 
with proceedings.
The submitted manuscript should follow the AAAI 2025 paper template.
Submissions will be rejected without review if they:
Contain more than 4 pages (excluding references and appendix).
Violate the double-blind policy.
Violate the dual-submission policy for papers.
The accepted papers will be publicly accessible on OpenReview, but the 
workshop is non-archival and does not have formal proceedings.
Papers will be peer-reviewed under a double-blind policy and must be 
submitted online through the OpenReview submission system.


-- 
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Laure Berti-Equille
IRD ESPACE-DEV
Institut de Recherche pour le Développement
https://laureberti.github.io/website/
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