LLMEval3

Overview

LLMEval3 is a large model evaluation benchmark launched by the Fudan University NLP Laboratory, positioned for AI model evaluation, with a focus on assessing large language models' capabilities in professional knowledge understanding and generative question answering. Compared with general capability tests, LLMEval-3 is more focused on discipline-based and systematic knowledge evaluation, making it suitable for observing models' real performance in professional fields.

This evaluation benchmark covers the 13 disciplinary categories designated by the Ministry of Education, including philosophy, economics, law, education, literature, history, science, engineering, agriculture, medicine, military science, management, and arts, and is further subdivided into more than 50 secondary disciplines. The overall question bank contains about 200,000 standard generative question-answering items, providing a relatively broad and dense reference for professional knowledge evaluation of models.

For research institutions, model teams, and developers concerned with models' performance in professional capabilities, LLMEval3 can serve as one of the evaluation benchmarks for comparing disciplinary capabilities across different large models and testing adaptability to professional scenarios.

Key Features

• Professional knowledge capability evaluation

  • Focuses on evaluating large models' mastery, understanding, and answering capabilities in professional disciplines.
  • Suitable for testing whether a model has the foundational capabilities to move from general knowledge toward professional applications.

• Covers 13 disciplinary categories

  • Built around the Ministry of Education's disciplinary system, with a relatively broad disciplinary scope.
  • Includes multiple directions such as humanities and social sciences, science, engineering, agriculture, and medicine, military studies, and the arts.

• Subdivided into more than 50 secondary disciplines

  • The evaluation granularity does not stop at the level of primary disciplines, but goes further into more specialized fields.
  • Helps more specifically identify a model's strengths and shortcomings in certain types of disciplines.

• About 200,000 standard generative question-answering items

  • Uses a large-scale question bank to support evaluation and can provide richer test samples.
  • Suitable for horizontal model comparison, phased capability tracking, and benchmark research.

• Oriented toward large model benchmark testing scenarios

  • Can serve as a reference tool for academic research, model training effect validation, and comparative analysis of professional capabilities.
  • Has certain value for teams focused on Chinese or multidisciplinary knowledge evaluation.

Product Pricing

At present, the publicly available information does not clearly provide LLMEval3 pricing details. Based on existing introductions, it is more oriented toward an evaluation benchmark and research project. As for whether it is open for use and whether permission is required, it is recommended to refer to the latest information on the official website:

• Official website: http://llmeval.com/index

Frequently Asked Questions

What does LLMEval3 mainly evaluate?

LLMEval3 mainly evaluates large language models' professional knowledge capabilities, especially their generative question-answering performance across different disciplines, rather than only general chat or common-knowledge answering ability.

Which disciplines does LLMEval3 cover?

It covers the 13 disciplinary categories designated by the Ministry of Education and includes more than 50 secondary disciplines, with a relatively comprehensive disciplinary scope.

How large is LLMEval3's question bank?

According to public introductions, LLMEval-3 contains about 200,000 standard generative question-answering items in total.

Which users is it suitable for?

It is relatively suitable for the following groups:

• Large model R&D teams
• AI evaluation researchers
• Developers focused on disciplinary capability performance
• Natural language processing researchers in universities and research institutions

Are there any latest feature descriptions or screenshots?

There is currently relatively little publicly captured information from the official website, and no more detailed feature pages or available images have been found yet. If you need to learn about the latest evaluation settings, data descriptions, or usage methods, it is recommended to visit the official website directly.