StructEval: Deepen and Broaden Large Language Model Assessment via
  Structured Evaluation
Authors: name: Boxi Cao, name: Mengjie Ren, name: Hongyu Lin, name: Xianpei Han, name: Feng Zhang, name: Junfeng Zhan, name: Le Sun

Evaluation is the baton for the development of large language models. Current
evaluations typically employ a single-item assessment paradigm for each atomic
test objective, which struggles to discern whether a model genuinely possesses
the required capabilities or merely memorizes/guesses the answers to specific
questions. To this end, we propose a novel evaluation framework referred to as
StructEval. Starting from an atomic test objective, StructEval deepens and
broadens the evaluation by conducting a structured assessment across multiple
cognitive levels and critical concepts, and therefore offers a comprehensive,
robust and consistent evaluation for LLMs. Experiments on three widely-used
benchmarks demonstrate that StructEval serves as a reliable tool for resisting
the risk of data contamination and reducing the interference of potential
biases, thereby providing more reliable and consistent conclusions regarding
model capabilities. Our framework also sheds light on the design of future
principled and trustworthy LLM evaluation protocols.