5 citations · 7 across the 5 of their papers we have counts for
6 papers
AIPsychoBench: Understanding the Psychometric Differences between LLMs and Humans
Wei Xie, Shuoyoucheng Ma, Zhenhua Wang +4
Large Language Models (LLMs) with hundreds of billions of parameters have exhibited human-like intelligence by learning from vast amounts of internet-scale data. However, the unint…
League of LLMs: A Benchmark-Free Paradigm for Mutual Evaluation of Large Language Models
Qianhong Guo, Wei Xie, Xiaofang Cai +7
Although large language models (LLMs) have shown exceptional capabilities across a wide range of tasks, reliable evaluation remains a critical challenge due to data contamination,…
Do Large Language Models Truly Grasp Mathematics? An Empirical Exploration From Cognitive Psychology
Wei Xie, Shuoyoucheng Ma, Zhenhua Wang +4
The cognitive mechanism by which Large Language Models (LLMs) solve mathematical problems remains a widely debated and unresolved issue. Currently, there is little interpretable ex…
Foot In The Door: Understanding Large Language Model Jailbreaking via Cognitive Psychology
Zhenhua Wang, Wei Xie, Baosheng Wang +4
Large Language Models (LLMs) have gradually become the gateway for people to acquire new knowledge. However, attackers can break the model's security protection ("jail") to access…
Self-Deception: Reverse Penetrating the Semantic Firewall of Large Language Models
Zhenhua Wang, Wei Xie, Kai Chen +3
Large language models (LLMs), such as ChatGPT, have emerged with astonishing capabilities approaching artificial general intelligence. While providing convenience for various socie…
HyperGo: Probability-based Directed Hybrid Fuzzing
Peihong Lin, Pengfei Wang, Xu Zhou +3
Directed grey-box fuzzing (DGF) is a target-guided fuzzing intended for testing specific targets (e.g., the potential buggy code). Despite numerous techniques proposed to enhance d…