Large model evaluation

A comprehensive evaluation tool for Chinese big models, which truly reflects the general ability of big models through a multi-dimensional and multi-perspective evaluation system, and helps technical progress and industrialization development.

An open-source big model capability assessment system designed to comprehensively and quantitatively assess the capabilities of big models in knowledge, language, understanding, reasoning, etc., and to drive iterative optimization of the models.

Initiated by Stanford University, it aims to comprehensively assess the capabilities of big language models through multiple dimensions and scenarios in order to drive technological advancement and model optimization of the evaluation benchmark.

A multimodal benchmarking framework designed to comprehensively assess and understand the performance of multimodal models in different scenarios, providing robust and reliable evaluation results through a well-designed evaluation process and labeled datasets.

It is a comprehensive assessment platform focusing on evaluating the general ability of large models in human cognition and problem solving tasks, which is jointly created by well-known universities and organizations, providing diversified assessment methods and authoritative rankings to help the development and application of AI technology.

The Chinese Basic Model Assessment Suite, jointly launched by Shanghai Jiao Tong University, Tsinghua University and the University of Edinburgh, covers objective questions assessed in multiple domains and difficulty levels, aiming to measure the ability of the Big Model in Chinese comprehension and reasoning.

A comprehensive, scientific, and fair big model evaluation system and open platform aims to help researchers assess the performance of basic models and training algorithms in an all-round way by providing multi-dimensional evaluation tools and methods.