{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/omnibench-towards-the-future-of-universal","title":"OmniBench: Towards The Future of Universal Omni-Language Models","arxiv_id":"2409.15272","date":"2024-09-23","proceeding":null,"authors":["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"],"abstract":"Recent advancements in multimodal large language models (MLLMs) have focused on integrating multiple modalities, yet their ability to simultaneously process and reason across different inputs remains underexplored. We introduce OmniBench, a novel benchmark designed to evaluate models' ability to recognize, interpret, and reason across visual, acoustic, and textual inputs simultaneously. We define language models capable of such tri-modal processing as omni-language models (OLMs). OmniBench features high-quality human annotations that require integrated understanding across all modalities. Our evaluation reveals that: i) open-source OLMs show significant limitations in instruction-following and reasoning in tri-modal contexts; and ii) most baseline models perform poorly (around 50% accuracy) even with textual alternatives to image/audio inputs. To address these limitations, we develop OmniInstruct, an 96K-sample instruction tuning dataset for training OLMs. We advocate for developing more robust tri-modal integration techniques and training strategies to enhance OLM performance. Codes and data could be found at our repo (https://github.com/multimodal-art-projection/OmniBench).","url_abs":"https://arxiv.org/abs/2409.15272v4","url_pdf":"https://arxiv.org/pdf/2409.15272v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"omnibench-towards-the-future-of-universal","repo_url":"https://github.com/multimodal-art-projection/omnibench","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"instruction-following","task_name":"Instruction Following"}],"methods":[{"method_slug":"focus","method_name":"Focus"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2409.15272","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}