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

• URL: http://arxiv.org/abs/2309.16058v1
• Date: Wed, 27 Sep 2023 22:50:51 GMT
• Title: AnyMAL: An Efficient and Scalable Any-Modality Augmented Language Model
• Authors: 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
• Abstract summary: We present Any-Modality Augmented Language Model (AnyMAL), a unified model that reasons over diverse input modality signals. AnyMAL inherits the powerful text-based reasoning abilities of the state-of-the-art LLMs including LLaMA-2 (70B) We conduct comprehensive empirical analysis comprising both human and automatic evaluations, and demonstrate state-of-the-art performance on various multimodal tasks.
• Score: 33.072967177313025
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: We present Any-Modality Augmented Language Model (AnyMAL), a unified model that reasons over diverse input modality signals (i.e. text, image, video, audio, IMU motion sensor), and generates textual responses. AnyMAL inherits the powerful text-based reasoning abilities of the state-of-the-art LLMs including LLaMA-2 (70B), and converts modality-specific signals to the joint textual space through a pre-trained aligner module. To further strengthen the multimodal LLM's capabilities, we fine-tune the model with a multimodal instruction set manually collected to cover diverse topics and tasks beyond simple QAs. We conduct comprehensive empirical analysis comprising both human and automatic evaluations, and demonstrate state-of-the-art performance on various multimodal tasks.

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