For Large Language Models (LLMs), a disconnect persists between benchmark performance and real-world utility. Current evaluation frameworks remain fragmented, prioritizing technical metrics while neglecting holistic assessment for deployment. This survey introduces an anthropomorphic evaluation paradigm through the lens of human intelligence, proposing a novel three-dimensional taxonomy: Intelligence Quotient (IQ)-General Intelligence for foundational capacity, Emotional Quotient (EQ)-Alignment Ability for value-based interactions, and Professional Quotient (PQ)-Professional Expertise for specialized proficiency. For practical value, we pioneer a Value-oriented Evaluation (VQ) framework assessing economic viability, social impact, ethical alignment, and environmental sustainability. Our modular architecture integrates six components with an implementation roadmap. Through analysis of 200+ benchmarks, we identify key challenges including dynamic assessment needs and interpretability gaps. It provides actionable guidance for developing LLMs that are technically proficient, contextually relevant, and ethically sound. We maintain a curated repository of open-source evaluation resources at: https://github.com/onejune2018/Awesome-LLM-Eval.
@article{arxiv.2508.18646,
title = {Beyond Benchmark: LLMs Evaluation with an Anthropomorphic and Value-oriented Roadmap},
author = {Jun Wang and Ninglun Gu and Kailai Zhang and Zijiao Zhang and Yelun Bao and Jin Yang and Xu Yin and Liwei Liu and Yihuan Liu and Pengyong Li and Gary G. Yen and Junchi Yan},
journal= {arXiv preprint arXiv:2508.18646},
year = {2025}
}