The rapid progress of foundation models and large language models (LLMs) has fueled significantly improvement in the capabilities of machine learning systems that benefit from mutlimodal input data. However, existing multimodal models are
predominantly built on top of pre-trained LLMs, which can limit accurate modeling of temporal dependencies across other modalities and thus limit the model’s ability to jointly process and leverage multimodal inputs. To specifically investigate
the alignment of text, video, and speech modalities in LLM-style (decoder-only) models, we consider a simplified…
predominantly built on top of pre-trained LLMs, which can limit accurate modeling of temporal dependencies across other modalities and thus limit the model’s ability to jointly process and leverage multimodal inputs. To specifically investigate
the alignment of text, video, and speech modalities in LLM-style (decoder-only) models, we consider a simplified…