SeSE: A Structural Information-Guided Uncertainty Quantification Framework for Hallucination Detection in LLMs
Published:Nov 20, 2025 11:54
•1 min read
•ArXiv
Analysis
This article introduces a new framework, SeSE, for detecting hallucinations in Large Language Models (LLMs). The framework leverages structural information to quantify uncertainty, which is a key aspect of identifying potentially false or fabricated information generated by LLMs. The source is ArXiv, indicating it's a research paper.
Key Takeaways
- •SeSE is a new framework for hallucination detection in LLMs.
- •It uses structural information to quantify uncertainty.
- •The goal is to identify potentially false information generated by LLMs.
Reference
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