In this research, we demonstrate that by converting various modalities such as images, videos, and audios into discrete tokens, we can effectively utilize a single language model architecture for the generation of diverse multimedia content.
Our experimental results illustrate that the integration of different modalities can enrich the capabilities of large language models, expanding their utility beyond text generation into the realms of video and audio.
The findings underline the importance of careful task prompt design and training strategies when employing large language models for multimedia content generation, highlighting both their potential and existing limitations.
Through comparative analysis, we show a significant improvement in performance over state-of-the-art methods, emphasizing the effectiveness of a unified approach to handling diverse data types.
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