AnyMAL: An Efficient and Scalable Any-Modality Augmented Language Model Seungwhan Moon author Andrea Madotto author Zhaojiang Lin author Tushar Nagarajan author Matt Smith author Shashank Jain author Chun-Fu Yeh author Prakash Murugesan author Peyman Heidari author Yue Liu author Kavya Srinet author Babak Damavandi author Anuj Kumar author 2024-11 text Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track Franck Dernoncourt editor Daniel Preoţiuc-Pietro editor Anastasia Shimorina editor Association for Computational Linguistics Miami, Florida, US conference publication moon-etal-2024-anymal 10.18653/v1/2024.emnlp-industry.98 https://aclanthology.org/2024.emnlp-industry.98/ 2024-11 1314 1332