Bridging Modalities Through Language in AI


The field of artificial intelligence (AI) has made significant strides, particularly with the advent of multimodal large language models (MLLMs), which demonstrate exceptional understanding across various modalities. One of the pioneering advancements in this arena is OneLLM, a novel framework presented by researchers Jiaming Han, Kaixiong Gong, Yiyuan Zhang, Jiaqi Wang, Kaipeng Zhang, Dahua Lin, Yu Qiao, Peng Gao, and Xiangyu Yue from MMLab, The Chinese University of Hong Kong, and Shanghai Artificial Intelligence Laboratory. OneLLM distinguishes itself by aligning eight distinct modalities to language using a unified framework, marking a significant leap from traditional models that depend on modality-specific encoders with limitations in architecture and modality scope.

OneLLM’s architecture introduces a universal encoder and a progressive multimodal alignment pipeline. This innovative approach starts with training an image projection module, followed by the creation of a universal projection module (UPM) that integrates various image projection modules with dynamic routing. The model progressively aligns additional modalities to the language large model (LLM) using UPM, paving the way for a comprehensive understanding and interaction across modalities including image, audio, video, point cloud, depth/normal map, IMU, and fMRI brain activity.

To harness OneLLM's instruction-following capabilities fully, the team curated a comprehensive multimodal instruction dataset encompassing 2M items, enabling OneLLM to excel in tasks like multimodal captioning, question answering, and reasoning. The performance of OneLLM, evaluated across 25 diverse benchmarks, is nothing short of remarkable, showcasing superior abilities compared to specialized models and existing MLLMs.

In essence, OneLLM represents a unified approach to multimodal understanding in AI, overcoming the traditional barriers set by modality-specific models. Its ability to seamlessly integrate diverse modalities within a single framework while delivering unparalleled performance across a wide range of tasks is a testament to the potential of unified models in advancing the frontiers of AI.


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