大语言模型心理测量学：评估、验证与增强的系统综述
Large Language Model Psychometrics: A Systematic Review of Evaluation, Validation, and Enhancement
AI总结：
本文系统综述了大语言模型的心理测量学，通过整合心理测量工具和理论，提升LLM的评估、理解和增强能力，推动以人类为中心的AI发展。
摘要：
大语言模型（LLMs）的发展速度超过了传统评估方法。这一进展带来了新的挑战，例如测量类人心理构念、超越静态和任务特定的基准以及建立以人类为中心的评估。这些挑战与心理测量学交叉，即量化人类心理的无形方面，如性格、价值观和智力。本文综述论文介绍了新兴的跨学科领域LLM心理测量学，利用心理测量工具、理论和原理来评估、理解和增强LLMs。所审查的文献系统地塑造了基准原则，拓宽了评估范围，精炼了方法，验证了结果，并推进了LLM能力。多样化的观点被整合，为跨学科研究人员提供了一个结构化的框架，使他们能够更全面地理解这一新兴领域。最终，综述为开发未来与人类水平AI相一致的评估范式提供了可行见解，以促进以人类为中心的AI系统的发展，造福社会。一个经过精心挑选的LLM心理测量学资源库可在https://github.com/valuebyte-ai/Awesome-LLM-Psychometrics上找到。
英文摘要：
The advancement of large language models (LLMs) has outpaced traditional evaluation methodologies. This progress presents novel challenges, such as measuring human-like psychological constructs, moving beyond static and task-specific benchmarks, and establishing human-centered evaluation. These challenges intersect with psychometrics, the science of quantifying the intangible aspects of human psychology, such as personality, values, and intelligence. This review paper introduces and synthesizes the emerging interdisciplinary field of LLM Psychometrics, which leverages psychometric instruments, theories, and principles to evaluate, understand, and enhance LLMs. The reviewed literature systematically shapes benchmarking principles, broadens evaluation scopes, refines methodologies, validates results, and advances LLM capabilities. Diverse perspectives are integrated to provide a structured framework for researchers across disciplines, enabling a more comprehensive understanding of this nascent field. Ultimately, the review provides actionable insights for developing future evaluation paradigms that align with human-level AI and promote the advancement of human-centered AI systems for societal benefit. A curated repository of LLM psychometric resources is available at https://github.com/valuebyte-ai/Awesome-LLM-Psychometrics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
