  Many existing benchmarks of large (multimodal) language models (LLMs) focus
on measuring LLMs' academic proficiency, often with also an interest in
comparing model performance with human test takers. While these benchmarks have
proven key to the development of LLMs, they suffer from several limitations,
including questionable measurement quality (e.g., Do they measure what they are
supposed to in a reliable way?), lack of quality assessment on the item level
(e.g., Are some items more important or difficult than others?) and unclear
human population reference (e.g., To whom can the model be compared?). In
response to these challenges, we propose leveraging knowledge from
psychometrics - a field dedicated to the measurement of latent variables like
academic proficiency - into LLM benchmarking. We make three primary
contributions. First, we introduce PATCH: a novel framework for
{P}sychometrics-{A}ssis{T}ed ben{CH}marking of LLMs. PATCH addresses the
aforementioned limitations, presenting a new direction for LLM benchmark
research. Second, we implement PATCH by measuring GPT-4 and Gemini-Pro-Vision's
proficiency in 8th grade mathematics against 56 human populations. We show that
adopting a psychometrics-based approach yields evaluation outcomes that diverge
from those based on existing benchmarking practices. Third, we release 4
high-quality datasets to support measuring and comparing LLM proficiency in
grade school mathematics and science against human populations.
