2 citations · 2 across the 2 of their papers we have counts for
3 papers
cs.CL2024
FreeEval: A Modular Framework for Trustworthy and Efficient Evaluation of Large Language Models
Zhuohao Yu, Chang Gao, Wenjin Yao +6
The rapid development of large language model (LLM) evaluation methodologies and datasets has led to a profound challenge: integrating state-of-the-art evaluation techniques cost-e…
cs.SE2024★ 2 cited
CodeShell Technical Report
Rui Xie, Zhengran Zeng, Zhuohao Yu +3
Code large language models mark a pivotal breakthrough in artificial intelligence. They are specifically crafted to understand and generate programming languages, significantly boo…
cs.CL2024
KIEval: A Knowledge-grounded Interactive Evaluation Framework for Large Language Models
Zhuohao Yu, Chang Gao, Wenjin Yao +6
Automatic evaluation methods for large language models (LLMs) are hindered by data contamination, leading to inflated assessments of their effectiveness. Existing strategies, which…