6 papers · 1 filter
Hidden Human-Like Nature of Machine-Generated Texts: Theory and Detection Enhancement
Chenwang Wu, Yiu-ming Cheung, Bo Han +1
Machine-generated texts (MGTs) produced by large language models (LLMs) are increasingly prevalent across various applications, while their potential misuse in fake news propagatio…
Multi-Level Contextual Token Relation Modeling for Machine-Generated Text Detection
Chenwang Wu, Yiuming Cheung, Bo Han +2
Machine-generated texts (MGTs) pose risks such as disinformation and phishing, underscoring the need for reliable detection. Metric-based methods, which extract statistically disti…
Beyond Raw Detection Scores: Markov-Informed Calibration for Boosting Machine-Generated Text Detection
Chenwang Wu, Yiu-ming Cheung, Shuhai Zhang +2
While machine-generated texts (MGTs) offer great convenience, they also pose risks such as disinformation and phishing, highlighting the need for reliable detection. Metric-based m…
Advancing Machine-Generated Text Detection from an Easy to Hard Supervision Perspective
Chenwang Wu, Yiu-ming Cheung, Bo Han +1
Existing machine-generated text (MGT) detection methods implicitly assume labels as the "golden standard". However, we reveal boundary ambiguity in MGT detection, implying that tra…
Learning to Substitute Components for Compositional Generalization
Zhaoyi Li, Gangwei Jiang, Chenwang Wu +3
Despite the rising prevalence of neural language models, recent empirical evidence suggests their deficiency in compositional generalization. One of the current de-facto solutions…
Understanding Privacy Risks of Embeddings Induced by Large Language Models
Zhihao Zhu, Ninglu Shao, Defu Lian +4
Large language models (LLMs) show early signs of artificial general intelligence but struggle with hallucinations. One promising solution to mitigate these hallucinations is to sto…