AnyMAL: An Efficient and Scalable Any-Modality Augmented Language Model. - Seungwhan Moon; Andrea Madotto; Zhaojiang Lin; Tushar Nagarajan; Matt Smith; Shashank Jain; Chun-Fu Yeh; Prakash Murugesan; Peyman Heidari; Yue Liu; Kavya Srinet; Babak Damavandi; et al. (EMNLP (Industry Track), 2024)