Evaluating the Generation Capabilities of Large Chinese Language Models

A new automated framework called CG-Eval has been introduced to benchmark the text-generation capabilities of large Chinese language models across six academic and professional domains, according to a paper posted on arXiv [1]. The framework is described as the first comprehensive, automated evaluation system designed specifically for Chinese language models [1][2]. It tests models on their ability to produce accurate, contextually appropriate answers in Science and Engineering, Humanities and Social Sciences, Mathematical Calculations, Medical Practitioner Qualification Examination, Judicial Examination, and Certified Public Accountant Examination [1][2]. The evaluation process is fully automated, which the authors state ensures objective and consistent comparisons across different models [1][2]. A central component of the framework is Gscore, a composite index calculated from a weighted sum of multiple metrics. Gscore automates the measurement of text-generation quality against reference standards, offering a detailed performance assessment [1][2]. The test data and comparative results are hosted at a dedicated website [1][2]. The paper was initially submitted on 9 August 2023 and was most recently revised on 27 May 2026 [1]. The work arrives as large language models, or LLMs, have become foundational to modern chatbots and other natural language processing applications [3]. LLMs are neural networks trained on vast text corpora to generate, summarize, and translate language [3]. Benchmark evaluations for these models typically seek to measure reasoning, factual accuracy, and safety [3]. While major LLM families such as Meta’s Llama and Google DeepMind’s Gemini have been widely deployed and studied, the CG-Eval framework specifically targets the evaluation gap for Chinese-language models [4][5]. Meta released the first Llama model in February 2023, and the latest version, Llama 4, was introduced in April 2025 before being succeeded by Muse Spark in April 2026 [4]. Google DeepMind announced its Gemini family of multimodal models in December 2023 [5]. The CG-Eval authors, including Hui Zeng, aim to provide a structured, automated alternative for assessing how Chinese LLMs perform on specialized, high-stakes examination content [1][2].