January 1, 2024Open Access

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

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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. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track. 2024.

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www.synapsesocial.com/papers/69de0b8da051b8e25be93a38 — DOI: https://doi.org/10.18653/v1/2024.emnlp-industry.98

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Seungwhan Moon

Andrea Madotto

Zhaojiang Lin

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

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Abstract

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Cite this study

Authors

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