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Although instruction-tuned large language models (LLMs) have exhibitedremarkable capabilities across various NLP tasks, their effectiveness on otherdata modalities beyond text has not been fully studied. In this work, wepropose Macaw-LLM, a novel multi-modal LLM that seamlessly integrates visual,audio, and textual information. Macaw-LLM consists of three main components: amodality module for encoding multi-modal data, a cognitive module forharnessing pretrained LLMs, and an alignment module for harmonizing diverserepresentations. Our novel alignment module seamlessly bridges multi-modalfeatures to textual features, simplifying the adaptation process from themodality modules to the cognitive module. In addition, we construct alarge-scale multi-modal instruction dataset in terms of multi-turn dialogue,including 69K image instances and 50K video instances. We have made our data,code and model publicly available, which we hope can pave the way for futureresearch in multi-modal LLMs and expand the capabilities of LLMs to handlediverse data modalities and address complex real-world scenarios.

Macaw-LLM: Multi-Modal Language Modeling with Image, Audio, Video, and Text Integration