aiqbench analysis
This paper addresses the critical gap between isolated benchmark scores and real-world LLM utility by proposing a diagnostic framework that maps evaluation metrics directly to the training pipeline. It introduces an anthropomorphic lens—incorporating IQ, PQ, EQ, and VQ—to guide developers toward models that are not only technically proficient but also contextually relevant and ethically sound. By analyzing over 200 public benchmarks, the work offers a strategic roadmap for shifting from static ranking to root-cause analysis in large language model development.
Beyond Benchmarks: LLM Evaluation with an Anthropomorphic and Lifecycle-oriented Roadmap
arXiv:2508.18646v3 Announce Type: replace Abstract: Despite their rapid advancement, large language models (LLMs) suffer from a critical disconnect between benchmark scores and real-world utility. Current evaluation remains fragmented, prioritizing isolated technical metrics over the holistic, developmental, and societal aspects essential for deployment. Rather than serving merely as a descriptive catalog, this work establishes a diagnostic ontology that causally maps evaluation dimensions to the canonical LLM training pipeline, transforming evaluation from static ranking into a diagnostic tool for root-cause analysis. In this paper, we introduce an anthropomorphic evaluation framework that re-conceptualizes LLM capabilities through a four-dimensional lens: Intelligence Quotient (IQ), Professional Quotient (PQ), Emotional Quotient (EQ), and Value-oriented Quotient (VQ). We operationalize these concepts through a modular evaluation architecture and validate the framework's diagnostic claims through meta-analysis of public benchmark trends. Analyzing over 200 benchmarks, we synthesize key challenges and future directions. This work offers a strategic compass for developing LLMs that are not only technically proficient but also contextually relevant and ethically sound. A curated repository is available at: https://github.com/onejune2018/Awesome-LLM-Eval.
Abstract:Despite their rapid advancement, large language models (LLMs) suffer from a critical disconnect between benchmark scores and real-world utility. Current evaluation remains fragmented, prioritizing isolated technical metrics over the holistic, developmental, and societal aspects essential for deployment. Rather than serving merely as a descriptive catalog, this work establishes a diagnostic ontology that causally maps evaluation dimensions to the canonical LLM training pipeline, transforming evaluation from static ranking into a diagnostic tool for root-cause analysis. In this paper, we introduce an anthropomorphic evaluation framework that re-conceptualizes LLM capabilities through a four-dimensional lens: Intelligence Quotient (IQ), Professional Quotient (PQ), Emotional Quotient (EQ), and Value-oriented Quotient (VQ). We operationalize these concepts through a modular evaluation architecture and validate the framework's diagnostic claims through meta-analysis of public benchmark trends. Analyzing over 200 benchmarks, we synthesize key challenges and future directions. This work offers a strategic compass for developing LLMs that are not only technically proficient but also contextually relevant and ethically sound. A curated repository is available at: this https URL.
Preprint. Under Review
Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
arXiv:2508.18646 [cs.AI]
(or arXiv:2508.18646v3 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2508.18646 Focus to learn more arXiv-issued DOI via DataCite
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