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Qiang Sheng

Chinese Academy of Sciences, Institute of Computing Technology, Beijing, China

18 papers at tracked venues · 10 at CORE A* · active 20242026

Venues

Frequent coauthors

Papers

  1. Beyond the Final Actor: Modeling the Dual Roles of Creator and Editor for Fine-Grained LLM-Generated Text Detection
  2. Logical Consistency as a Bridge: Improving LLM Hallucination Detection via Label Constraint Modeling between Responses and Self-Judgments
  3. Reasoning About the Unsaid: Misinformation Detection with Omission-Aware Graph Inference
  4. Tailoring Rumor Debunking to You: Diversifying Chinese Rumor-Debunking Passages with an LLM-Driven Simulated Feedback-Enhanced Framework
  5. Bridging Thoughts and Words: Graph-Based Intent-Semantic Joint Learning for Fake News Detection
  6. Combating Online Misinformation Videos: Characterization, Detection, and Prevention
    ACM MM 2025 · Qiang Sheng
  7. Enhancing Fake News Video Detection via LLM-Driven Creative Process Simulation
  8. Enhancing the Comprehensibility of Text Explanations via Unsupervised Concept Discovery
  9. Forewarned is Forearmed: Pre-Synthesizing Jailbreak-like Instructions to Enhance LLM Safety Guardrail to Potential Attacks
  10. From Judgment to Interference: Early Stopping LLM Harmful Outputs via Streaming Content Monitoring
  11. LLM-Generated Fake News Induces Truth Decay in News Ecosystem: A Case Study on Neural News Recommendation
  12. The Staircase of Ethics: Probing LLM Value Priorities through Multi-Step Induction to Complex Moral Dilemmas
  13. Bad Actor, Good Advisor: Exploring the Role of Large Language Models in Fake News Detection
  14. FakingRecipe: Detecting Fake News on Short Video Platforms from the Perspective of Creative Process
  15. Let Silence Speak: Enhancing Fake News Detection with Generated Comments from Large Language Models
  16. Preventing and Detecting Misinformation Generated by Large Language Models
  17. Ten Words Only Still Help: Improving Black-Box AI-Generated Text Detection via Proxy-Guided Efficient Re-Sampling
  18. Vision-fused Attack: Advancing Aggressive and Stealthy Adversarial Text against Neural Machine Translation