Summary of Omnixr: Evaluating Omni-modality Language Models on Reasoning Across Modalities, by Lichang Chen and Hexiang Hu and Mingda Zhang and Yiwen Chen and Zifeng Wang and Yandong Li and Pranav Shyam and Tianyi Zhou and Heng Huang and Ming-hsuan Yang and Boqing Gong
OmnixR: Evaluating Omni-modality Language Models on Reasoning across Modalities
by Lichang Chen, Hexiang Hu, Mingda Zhang, Yiwen Chen, Zifeng Wang, Yandong Li, Pranav Shyam, Tianyi Zhou, Heng Huang, Ming-Hsuan Yang, Boqing Gong
First submitted to arxiv on: 16 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Multimedia (cs.MM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary OmnixR is an evaluation suite designed to assess the performance of state-of-the-art Omni-modality Language Models (OLMs) like GPT-4o and Gemini. The primary challenge in evaluating OLMs lies in their ability to integrate information from multiple modalities, such as text, vision, and audio, to accomplish tasks. Existing benchmarks focus on single or dual-modality tasks, neglecting comprehensive assessments of model reasoning across modalities. OmnixR addresses this gap by introducing two evaluation variants: a synthetic dataset (Omnify) generated by translating text into various modalities (audio, images, video, and hybrids), and a realistic subset curated by experts for evaluating cross-modal reasoning in natural settings. The suite provides a rigorous testbed for assessing OLMs’ ability to integrate information from multiple modalities, highlighting the challenges of omni-modal AI alignment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary OmnixR is a special tool that helps scientists evaluate how well language models can understand and use different types of information like text, pictures, and sounds. These models are very good at understanding one type of information, but they struggle when they need to combine multiple types of information to answer a question. OmnixR has two ways to test these models: one is fake data that looks like real-life scenarios, and the other is real-world data carefully checked by experts. The tool shows that even the best language models have trouble understanding and combining different types of information. |
Keywords
» Artificial intelligence » Alignment » Gemini » Gpt
