In the rapidly evolving landscape of Large Language Models (LLMs), introduction of well-defined and standardized evaluation methodologies remains a crucial challenge. This paper traces the historical trajectory of LLM evaluations, from the foundational questions posed by Alan Turing to the modern era of AI research. We categorize the evolution of LLMs into distinct periods, each characterized by its unique benchmarks and evaluation criteria. As LLMs increasingly mimic human-like behaviors, traditional evaluation proxies, such as the Turing test, have become less reliable. We emphasize the pressing need for a unified evaluation system, given the broader societal implications of these models. Through an analysis of common evaluation methodologies, we advocate for a qualitative shift in assessment approaches, underscoring the importance of standardization and objective criteria. This work serves as a call for the AI community to collaboratively address the challenges of LLM evaluation, ensuring their reliability, fairness, and societal benefit.

大语言模型的评估方法学的引入和标准化是一个重要的挑战，本文追溯了LLM评估的历史轨迹，从Alan Turing提出的基础问题到现代人工智能研究的时代。我们将LLM的发展划分为不同的时期，每个时期都有其独特的基准和评估标准。随着LLM越来越像人类行为，传统的评估指标，如图灵测试，变得不太可靠了。我们强调了对统一评估体系的迫切需求，考虑到这些模型的更广泛的社会影响。通过对常见评估方法的分析，我们主张在评估方法上进行定性转变，强调标准化和客观标准的重要性。本研究呼吁人工智能社区共同解决LLM评估的挑战，确保其可靠性、公正性和社会利益。

后图灵：LLM评估地图绘制