47 citations · 153 across the 19 of their papers we have counts for
4 papers · 1 filter
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…
Spiral of Silence: How is Large Language Model Killing Information Retrieval? -- A Case Study on Open Domain Question Answering
Xiaoyang Chen, Ben He, Hongyu Lin +5
The practice of Retrieval-Augmented Generation (RAG), which integrates Large Language Models (LLMs) with retrieval systems, has become increasingly prevalent. However, the repercus…
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…
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…