# Workshop on Navigating and Addressing Data Problems for Foundation Models

Foundation models (FMs) have become central to modern machine learning, with data playing a crucial role in their development and sparking increased attention to data-related challenges such as curation and attribution. Adapting traditional data-centric methods to FMs is challenging due to the scale of both data and model architectures, necessitating interdisciplinary collaboration and community efforts. Building on the success of the first Data Problems in Foundation Models (DATA-FM) workshop at ICLR 2024, the second DATA-FM workshop will address persistent and emerging data-related challenges in FM deployment. While longstanding issues in data collection, curation, and synthesis remain relevant, new challenges have arisen as FMs are integrated into a growing number of applications and become increasingly multi-modal. Concurrently, the societal impact of AI has intensified, highlighting concerns such as data copyright. These evolving challenges emphasize the need for continued, focused discussions on data-related issues in FM development. Our goals include fostering a comprehensive understanding of these challenges across the entire FM pipeline and creating a platform for interdisciplinary researchers to connect, collaborate, and drive progress. We hope this workshop will serve as a catalyst for innovative solutions to critical data challenges, shaping the future of FMs and their wide-ranging applications.

We encourage submissions across a wide range of topics, including but not limited to:

- Data Collection and Curation for Foundation Models
  - Practical strategies for curating data (e.g., filtering, mixing, repairing) tailored to FM training stages.
  - Extending data curation techniques to Retrieval-Augmented Generation (RAG), multimodal settings, and LLM agents.
  - Theoretical frameworks for guiding data selection and scaling laws for foundation models.

- Data Attribution, Interpretability, and Data Marketplaces
  - Efficient techniques for attributing model outputs to specific training data.
  - Evaluating and comparing data attribution methods.
  - Economic models for data pricing and the design of data marketplaces that ensure fair compensation.

- Legal and Technical Solutions for Data Copyright Protection
  - Mitigation strategies and mathematical frameworks for addressing copyright issues in FM training data.
  - Connections between copyright, privacy, and fairness, including adaptations of techniques like machine unlearning.

- Synthetic Data and Model Collapse
  - High-quality synthetic data generation and its impact on FM performance, robustness, and safety.
  - Understanding and mitigating model collapse through theoretical and empirical investigations.

- Data and Society (Safety, Privacy, Fairness, and Other Social Impacts)
  - Improving AI safety, privacy, and fairness through data-centric approaches.
  - Addressing the side effects of data curation on fairness and ethics in FMs.
 
    
- Benchmarks and Evaluations
  - Designing evaluation metrics for data-centric techniques and creating reliable dataset benchmarks for FMs.
  - Identifying and addressing pitfalls in existing dataset benchmarks, such as test data contamination.