OmniEvalKit: A Modular, Lightweight Toolbox for Evaluating Large Language Model and its Omni-Extensions
Yi-Kai Zhang, Xu-Xiang Zhong, Shiyin Lu, Qing-Guo Chen, De-Chuan Zhan, Han-Jia Ye·December 09, 2024
Summary
OmniEvalKit is a modular, lightweight benchmarking toolbox for evaluating Large Language Models (LLMs) and their omni-extensions across multilingual, multidomain, and multimodal tasks. It supports over 100 LLMs and 50 datasets, enabling comprehensive evaluations. Its modular architecture, with a Static Builder and Dynamic Data Flow, facilitates easy integration of new models and datasets. Aimed at providing an ultra-lightweight, fast-deployable evaluation framework for the AI community, OmniEvalKit enhances convenience and versatility in downstream applications.
Introduction
Background
Overview of Large Language Models (LLMs)
Importance of benchmarking in AI research
Challenges in evaluating LLMs across multilingual, multidomain, and multimodal tasks
Objective
To provide a modular, lightweight benchmarking toolbox for evaluating LLMs and their omni-extensions
To support a wide range of models and datasets for comprehensive evaluations
To offer an ultra-lightweight, fast-deployable evaluation framework for the AI community
Method
Data Collection
Overview of the datasets supported by OmniEvalKit
Methods for collecting and preparing data for evaluation
Data Preprocessing
Techniques for preprocessing data to ensure compatibility with different models
Handling of multilingual, multidomain, and multimodal data
Model Integration
Process for integrating new models into the framework
Utilization of a Static Builder and Dynamic Data Flow for flexibility
Evaluation Framework
Overview of the evaluation metrics and processes
Customizability of the evaluation framework for specific use cases
Features
Comprehensive Coverage
Support for over 100 LLMs
Integration of more than 50 datasets
Modularity
Easy addition of new models and datasets
Separation of concerns in the Static Builder and Dynamic Data Flow
Versatility
Application in various downstream AI tasks
Adaptability to different evaluation needs
Convenience
Lightweight and fast-deployable framework
Streamlined process for model and dataset integration
Applications
Research and Development
Enhancing model selection and optimization
Facilitating comparative studies among different LLMs
Education and Training
Providing a standardized evaluation platform for students and educators
Supporting curriculum development in AI and NLP
Industry Use
Accelerating the evaluation process in product development
Enabling rapid prototyping and testing of AI solutions
Conclusion
Summary of OmniEvalKit's contributions
Future directions and potential improvements
Call to action for the AI community
Basic info
papers
computation and language
computer vision and pattern recognition
multimedia
machine learning
artificial intelligence
Advanced features