activity
20232026
collaborators

8 papers

cs.CL2026

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…

cs.CL2026

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…

cs.CL2026

IceBreaker for Conversational Agents: Breaking the First-Message Barrier with Personalized Starters

Hongwei Zheng, Weiqi Wu, Zhengjia Wang +6

Conversational agents, such as ChatGPT and Doubao, have become essential daily assistants for billions of users. To further enhance engagement, these systems are evolving from pass…

cs.CL2025

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…

cs.CL2025

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…

cs.CL2025

The Staircase of Ethics: Probing LLM Value Priorities through Multi-Step Induction to Complex Moral Dilemmas

Ya Wu, Qiang Sheng, Danding Wang +5

Ethical decision-making is a critical aspect of human judgment, and the growing use of LLMs in decision-support systems necessitates a rigorous evaluation of their moral reasoning…