OmniBench: Towards The Future of Universal Omni-Language Models
Recent advancements in multimodal large language models (MLLMs) have aimed to
integrate and interpret data across diverse modalities. However, the capacity
of these models to concurrently process and reason about multiple modalities
remains inadequately explored, partly due to the lack of comprehensive
modality-wise benchmarks. We introduce OmniBench, a novel benchmark designed to
rigorously evaluate models' ability to recognize, interpret, and reason across
visual, acoustic, and textual inputs simultaneously. We define models capable
of such tri-modal processing as omni-language models (OLMs). OmniBench is
distinguished by high-quality human annotations, ensuring that accurate
responses require integrated understanding and reasoning across all three
modalities. Our main findings reveal that: i) open-source OLMs exhibit critical
limitations in instruction-following and reasoning capabilities within
tri-modal contexts; and ii) the baseline models perform poorly (below 50%
accuracy) even when provided with alternative textual representations of images
and audio. These results suggest that the ability to construct a consistent
context from text, image, and audio is often overlooked in existing MLLM
training paradigms. We advocate for future research to focus on developing more
robust tri-modal integration techniques and training strategies to enhance OLM
performance across diverse modalities. The codes and live leaderboard could be
found at https://m-a-p.ai/OmniBench.
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OmniBench: Towards The Future of Universal Omni-Language Models
Recent advancements in multimodal large language models (MLLMs) have aimed to
integrate and interpret data across diverse modalities. However, the capacity
of these models to concurrently process and reason about multiple modalities
remains inadequately explored, partly due to the lack of comprehensive
modality-wise benchmarks. We introduce OmniBench, a novel benchmark designed to
rigorously evaluate models' ability to recognize, interpret, and reason across
visual, acoustic, and textual inputs simultaneously. We define models capable
of such tri-modal processing as omni-language models (OLMs). OmniBench is
distinguished by high-quality human annotations, ensuring that accurate
responses require integrated understanding and reasoning across all three
modalities. Our main findings reveal that: i) open-source OLMs exhibit critical
limitations in instruction-following and reasoning capabilities within
tri-modal contexts; and ii) the baseline models perform poorly (below 50%
accuracy) even when provided with alternative textual representations of images
and audio. These results suggest that the ability to construct a consistent
context from text, image, and audio is often overlooked in existing MLLM
training paradigms. We advocate for future research to focus on developing more
robust tri-modal integration techniques and training strategies to enhance OLM
performance across diverse modalities. The codes and live leaderboard could be
found at https://m-a-p.ai/OmniBench.
OmniBench is a benchmark for evaluating multimodal large language models' ability to simultaneously process and reason across visual, acoustic, and textual inputs, revealing significant limitations in current models.
multimodal large language models OLMs OmniBench tri-modal processing instruction-following reasoning capabilities human annotations tri-modal contexts