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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, Anuj Kumar

VenueA*EMNLP
Year2024
ProceedingsEMNLP (Industry Track)

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