We propose a workshop on the emerging frontier at the intersection between statistics and foundation models. Rigorous evaluation of large foundation models such as LLMs is necessary for reliable deployment, but it poses a towering challenge due to a lack of datasets and the black-box nature of many such models. The proposed workshop brings together the community working on understanding and improving LLMs with new statistical methodologies, and explores topics including benchmarking, measuring and correcting bias, automatic evaluation, watermarking, models/data auditing, and uncertainty quantification.