Summary of Omnievalkit: a Modular, Lightweight Toolbox For Evaluating Large Language Model and Its Omni-extensions, by Yi-kai Zhang and Xu-xiang Zhong and Shiyin Lu and Qing-guo Chen and De-chuan Zhan and Han-jia Ye
OmniEvalKit: A Modular, Lightweight Toolbox for Evaluating Large Language Model and its Omni-Extensions
by Yi-Kai Zhang, Xu-Xiang Zhong, Shiyin Lu, Qing-Guo Chen, De-Chuan Zhan, Han-Jia Ye
First submitted to arxiv on: 9 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); 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 This paper introduces OmniEvalKit, a novel benchmarking toolbox designed to evaluate Large Language Models (LLMs) and their extensions across multilingual, multidomain, and multimodal capabilities. The toolbox provides a modular, lightweight, and automated evaluation system, supporting over 100 LLMs and 50 evaluation datasets. Unlike existing benchmarks that focus on single aspects, OmniEvalKit offers comprehensive evaluations across thousands of model-dataset combinations. The authors structured the framework with a modular architecture comprising a Static Builder and Dynamic Data Flow, enabling seamless integration of new models and datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine having a special tool to test how well big language machines can understand different languages, topics, and types of data. This paper creates just that – OmniEvalKit. It’s like a report card for these machines, showing how well they do on different tasks. The tool is easy to use and can test many different machines at once, making it useful for people working with AI. |
