10 papers
Beyond the Final Actor: Modeling the Dual Roles of Creator and Editor for Fine-Grained LLM-Generated Text Detection
Yang Li, Qiang Sheng, Zhengjia Wang +3
The misuse of large language models (LLMs) requires precise detection of synthetic text. Existing works mainly follow binary or ternary classification settings, which can only dist…
Logical Consistency as a Bridge: Improving LLM Hallucination Detection via Label Constraint Modeling between Responses and Self-Judgments
Hao Mi, Qiang Sheng, Shaofei Wang +7
Large Language Models (LLMs) are prone to factual hallucinations, risking their reliability in real-world applications. Existing hallucination detectors mainly extract micro-level…
Reasoning About the Unsaid: Misinformation Detection with Omission-Aware Graph Inference
Zhengjia Wang, Danding Wang, Qiang Sheng +2
This paper investigates the detection of misinformation, which deceives readers by explicitly fabricating misleading content or implicitly omitting important information necessary…
Forewarned is Forearmed: Pre-Synthesizing Jailbreak-like Instructions to Enhance LLM Safety Guardrail to Potential Attacks
Sheng Liu, Qiang Sheng, Danding Wang +3
Despite advances in improving large language model (LLM) to refuse to answer malicious instructions, widely used LLMs remain vulnerable to jailbreak attacks where attackers generat…
Bridging Thoughts and Words: Graph-Based Intent-Semantic Joint Learning for Fake News Detection
Zhengjia Wang, Qiang Sheng, Danding Wang +2
Fake news detection is an important and challenging task for defending online information integrity. Existing state-of-the-art approaches typically extract news semantic clues, suc…
Exploring news intent and its application: A theory-driven approach
Zhengjia Wang, Danding Wang, Qiang Sheng +3
Understanding the intent behind information is crucial. However, news as a medium of public discourse still lacks a structured investigation of perceived news intent and its applic…