47 citations · 153 across the 19 of their papers we have counts for
Showing 2024 · cs.CLShow all
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cs.CL2024★ 3 cited
Humanizing Machine-Generated Content: Evading AI-Text Detection through Adversarial Attack
Ying Zhou, Ben He, Le Sun
With the development of large language models (LLMs), detecting whether text is generated by a machine becomes increasingly challenging in the face of malicious use cases like the…
cs.CL2024
Rule or Story, Which is a Better Commonsense Expression for Talking with Large Language Models?
Ning Bian, Xianpei Han, Hongyu Lin +3
Building machines with commonsense has been a longstanding challenge in NLP due to the reporting bias of commonsense rules and the exposure bias of rule-based commonsense reasoning…
cs.CL2024★ 2 cited
Hidding the Ghostwriters: An Adversarial Evaluation of AI-Generated Student Essay Detection
Xinlin Peng, Ying Zhou, Ben He +2
Large language models (LLMs) have exhibited remarkable capabilities in text generation tasks. However, the utilization of these models carries inherent risks, including but not lim…