KIEval: A Knowledge-grounded Interactive Evaluation Framework for Large
  Language Models
Zhuohao Yu Chang Gao Wenjin Yao Yidong Wang Wei Ye Jindong Wang Xing Xie Yue Zhang Shikun Zhang
Automatic evaluation methods for large language models (LLMs) are hindered by
data contamination, leading to inflated assessments of their effectiveness.
Existing strategies, which aim to detect contaminated texts, focus on
quantifying contamination status instead of accurately gauging model
performance. In this paper, we introduce KIEval, a Knowledge-grounded
Interactive Evaluation framework, which incorporates an LLM-powered
"interactor" role for the first time to accomplish a dynamic
contamination-resilient evaluation. Starting with a question in a conventional
LLM benchmark involving domain-specific knowledge, KIEval utilizes dynamically
generated, multi-round, and knowledge-focused dialogues to determine whether a
model's response is merely a recall of benchmark answers or demonstrates a deep
comprehension to apply knowledge in more complex conversations. Extensive
experiments on seven leading LLMs across five datasets validate KIEval's
effectiveness and generalization. We also reveal that data contamination brings
no contribution or even negative effect to models' real-world applicability and
understanding, and existing contamination detection methods for LLMs can only
identify contamination in pre-training but not during supervised fine-tuning.