"AnyMAL: An Efficient and Scalable Any-Modality Augmented Language Model."

Seungwhan Moon et al. (2024)

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DOI: 10.18653/V1/2024.EMNLP-INDUSTRY.98

access: open

type: Conference or Workshop Paper

metadata version: 2025-06-13

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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:
AnyMAL: An Efficient and Scalable Any-Modality Augmented Language Model. EMNLP (Industry Track) 2024: 1314-1332

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