# Workshop on Foundation Models in the Wild

In the era of AI-driven transformations, foundation models (FMs) have become pivotal in various applications, from natural language processing to computer vision. These models, with their immense capabilities, reshape the future of scientific research and the broader human society, but also introduce challenges in their in-the-wild deployments. The Workshop on FMs in the wild delves into the urgent need for these models to be useful when deployed in our societies. The significance of this topic cannot be overstated, as the real-world implications of these models impact everything from daily information access to critical decision-making in fields like medicine and finance. Stakeholders, from developers to end-users, care deeply about this because the successful integration of FMs into in-the-wild frameworks necessitates a careful consideration of many properties, including adaptivity, reliability, efficiency, and reasoning ability.

## Key Problems We Aim to Address

- In-the-wild Adaptation: How can we leverage techniques such as Retrieval-Augmented Generation (RAG), In-context Learning (ICL), or Fine-tuning (FT) to adapt FMs for specific domains, such as drug discovery, education, or clinical health?
- Reasoning and Planning: How can FMs be enhanced to tackle more complex in-the-wild tasks that require multi-step reasoning or decision-making, such as multi-hop question answering, mathematical problem-solving, theorem proving, code generation, or robot planning scenarios?
- Reliability and Responsibility: How can FMs work reliably outside their training distribution? And how can we address issues like hallucination, fairness, ethics, safety and privacy within the society?
- Practical Limitations in Deployment: How can FMs tackle challenges in practical applications, such as system constraints, memory requirements, response time demands, data acquisition barriers, and computational costs for inference-time scaling and long-context input?

The Workshop on Foundation Models in the Wild@ICLR 2025 invite submissions from researchers in the fields of machine learning pertaining to foundation models and its in-the wild applications. Additionally, we welcome contributions from scholars in the natural sciences (such as physics, chemistry, and biology) and social sciences (including pedagogy and sociology) that necessitate the use of foundation models.

## Scope
We welcome contributions across a broad spectrum of topics, including but not limited to:

- Innovations in techniques for customizing models to individual user preferences, tasks, or domains
- Advancements in the reasoning and planning abilities of FMs in complex real-world challenges
- Theoretical and empirical investigations into the reliability and responsibility of various FMs
- Strategies for overcoming practical limitations (e.g., memory, time, data) of FMs in broad applications
- Methods for integrating multiple modalities (e.g., text, images, action) into a unified in-the-wild framework
- Discussions on FM agents that perform intricate tasks through interaction with the environment
- In-depth discussions exploring the in-the-wild deployments and applications of FMs
- Benchmark methodologies for assessing FMs in real-world settings