In the burgeoning field of artificial intelligence (AI), the unprecedented progress of large language models (LLMs) in natural language processing (NLP) offers an opportunity to revisit the entire approach of traditional metrics of machine intelligence, both in form and content. As the realm of machine cognitive evaluation has already reached Imitation, the next step is an efficient Language Acquisition and Understanding. Our paper proposes a paradigm shift from the established Turing Test towards an all-embracing framework that hinges on language acquisition, taking inspiration from the recent advancements in LLMs. The present contribution is deeply tributary of the excellent work from various disciplines, point out the need to keep interdisciplinary bridges open, and delineates a more robust and sustainable approach.

翻译：在人工智能（AI）蓬勃发展的领域中，大语言模型（LLMs）在自然语言处理（NLP）方面取得了前所未有的进展，这为我们重新审视传统机器智能度量标准在形式与内容上的整体方法提供了契机。当前机器认知评估已进入模仿阶段，下一步的关键在于高效的语言习得与理解。本文基于大语言模型的最新进展，提出从传统的图灵测试范式向一个以语言习称为核心的综合性评估框架的转变。本文的贡献深受多个学科优秀研究成果的启发，指出了保持跨学科桥梁畅通的必要性，并勾勒出一条更为稳健且可持续的发展路径。