StructEval: Deepen and Broaden Large Language Model Assessment via Structured Evaluation

Autor:	Cao, Boxi, Ren, Mengjie, Lin, Hongyu, Han, Xianpei, Zhang, Feng, Zhan, Junfeng, Sun, Le
Rok vydání:	2024
Předmět:	Computer Science - Computation and Language Computer Science - Artificial Intelligence Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: ACL 2024;Benchmark at https://github.com/c-box/StructEval ;Leaderboard at https://huggingface.co/spaces/Bowieee/StructEval_leaderboard
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2408.03281 Zobrazit plný text záznamu View this record from Arxiv