Open Science for Foundation Models

Foundation models (FMs) have revolutionized artificial intelligence (AI) research across many domains, enabling rapid adaptation to diverse downstream tasks. These FMs, trained on massive and high-quality datasets, have demonstrated remarkable performance in natural language processing (e.g., BERT [4], GPT [12], Gemini [14]), computer vision (e.g., ViT [5], VQGAN [6]), speech recognition (e.g., Whisper [13]), and multi-modal understanding (e.g., GPT-4o, LLaVA [10], QwenVL [2]). Despite these advancements, the scientific transparency and reproducibility of FMs have not kept pace. Proprietary interfaces conceal crucial details, such as training data, architectural design, and development processes, limiting scientific understanding of these models’ biases and risks. To bridge this gap, there is a growing need for truly open foundation models that the research community can access and study.In response, a surge of open science works has emerged to address the issue, encouraging transparency of FMs within the research community. Notable examples include open-access large language models (LLMs) such as Llama [15], Mistral [8], and Qwen [1], as well as extensive pre-training datasets like RedPajama [3] and The Stack [9]. These efforts have democratized access to high-performance models and sparked further innovation. Moreover, several initiatives like OLMo [7] and StarCoder [11] now offer fully transparent models, providing detailed insights into training protocols, intermediate checkpoints, and data processing pipelines. Such transparency is critical to fostering reproducibility and accelerating research across the field.Therefore, the first Open Science for Foundation Models (OS-FMs) workshop aims to bring together a community of researchers committed to open science, reproducible research, and the open-source movement within AI. This workshop seeks contributions that explore key aspects of FMs, such as dataset curation, evaluation methodologies, high-performing models, and efficient implementations. While models have become increasingly large in this era, the workshop promotes the open sharing of both small (e.g., 1B) and large models, as long as their conclusions are based on rigorous scientific experiments. By emphasizing scientific discovery and open sharing, the workshop seeks to address the growing inaccessibility of foundation models, ensuring that the benefits of AI advancements are disseminated across the global research community.