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.

翻译：在大语言模型（LLMs）快速发展的背景下，建立清晰定义且标准化的评估方法仍是一项关键挑战。本文追溯了从艾伦·图灵提出的基础性问题到现代人工智能研究时代的大语言模型评估历史轨迹。我们将大语言模型的发展划分为若干不同时期，每个时期以其独特的基准和评估标准为特征。随着大语言模型日益模仿人类行为，图灵测试等传统评估代理的可靠性逐渐降低。鉴于这些模型更广泛的社会影响，我们强调建立统一评估体系的迫切需求。通过对常见评估方法的分析，我们倡导评估方法的质的转变，强调标准化和客观标准的重要性。本工作旨在呼吁人工智能社区共同应对大语言模型评估的挑战，确保其可靠性、公平性和社会效益。