Add seminar

Author: feralvam

content/event/2025-05-01/index.md

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+---
+# Documentation: https://wowchemy.com/docs/managing-content/
+
+title: "Seminar: \"OmniBench, Towards The Future of Universal Omni-Language Models\""
+# event:
+# event_url:
+location: Abacws
+# address:
+#   street:
+#   city:
+#   region:
+#   postcode:
+#   country:
+summary: Talk by [Yizhi Li](https://yizhilll.github.io/) (University of Manchester)
+abstract: "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) these 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."
+
+# Talk start and end times.
+#   End time can optionally be hidden by prefixing the line with `#`.
+date: 2025-05-01T13:00:00Z
+date_end: 2025-05-01T14:00:00Z
+all_day: false
+
+# Schedule page publish date (NOT event date).
+publishDate: 2025-04-28T00:00:00Z
+
+authors: [ousidhoumn]
+tags: []
+
+# Is this a featured event? (true/false)
+featured: false
+
+# Featured image
+# To use, add an image named `featured.jpg/png` to your page's folder. 
+# Focal points: Smart, Center, TopLeft, Top, TopRight, Left, Right, BottomLeft, Bottom, BottomRight.
+image:
+  caption: ""
+  focal_point: ""
+  preview_only: false
+
+# Custom links (optional).
+#   Uncomment and edit lines below to show custom links.
+# links:
+# - name: Follow
+#   url: https://twitter.com
+#   icon_pack: fab
+#   icon: twitter
+
+# Optional filename of your slides within your event's folder or a URL.
+url_slides:
+
+url_code:
+url_pdf:
+url_video:
+
+# Markdown Slides (optional).
+#   Associate this event with Markdown slides.
+#   Simply enter your slide deck's filename without extension.
+#   E.g. `slides = "example-slides"` references `content/slides/example-slides.md`.
+#   Otherwise, set `slides = ""`.
+slides: ""
+
+# Projects (optional).
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+#   Simply enter your project's folder or file name without extension.
+#   E.g. `projects = ["internal-project"]` references `content/project/deep-learning/index.md`.
+#   Otherwise, set `projects = []`.
+projects: []
+---
+
+**Invited Speaker:** [Yizhi Li](https://yizhilll.github.io/) (University of Manchester)