Summary of Omnibench: Towards the Future Of Universal Omni-language Models, by Yizhi Li et al.
OmniBench: Towards The Future of Universal Omni-Language Models
by Yizhi Li, Ge Zhang, Yinghao Ma, Ruibin Yuan, Kang Zhu, Hangyu Guo, Yiming Liang, Jiaheng Liu, Zekun Wang, Jian Yang, Siwei Wu, Xingwei Qu, Jinjie Shi, Xinyue Zhang, Zhenzhu Yang, Xiangzhou Wang, Zhaoxiang Zhang, Zachary Liu, Emmanouil Benetos, Wenhao Huang, Chenghua Lin
First submitted to arxiv on: 23 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Recent advancements in multimodal large language models (MLLMs) have aimed to integrate and interpret data across diverse modalities. A novel benchmark, OmniBench, is introduced to evaluate MLLMs’ ability to recognize, interpret, and reason about visual, acoustic, and textual inputs simultaneously. The benchmark defines omni-language models (OLMs) as those capable of tri-modal processing. High-quality human annotations ensure that accurate responses require integrated understanding and reasoning across all three modalities. Findings reveal that most OLMs exhibit limitations in instruction-following and reasoning capabilities within tri-modal contexts, while baselines perform poorly even with textual representations of images or/and audio. The results highlight the importance of constructing a consistent context from text, image, and audio, which is often overlooked in existing MLLM training paradigms. To address this gap, an instruction tuning dataset, OmniInstruct, is curated for training OLMs to adapt to multimodal contexts. Future research should focus on developing more robust tri-modal integration techniques and training strategies to enhance OLM performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to test language models has been developed. These models can understand and work with different types of data, like pictures, sounds, and words. The test is called OmniBench and it helps us see how well the models can use all this information together. The results show that most models struggle to make sense of all three kinds of data at once. This means they need to be trained in a way that helps them work better with different types of data. The researchers created a special dataset to help train these models and make them more useful. |
Keywords
» Artificial intelligence » Instruction tuning
