[UAI 2026 Oral] SeSE: Black-Box Uncertainty Quantification for Large Language Models Based on Structural Information Theory, which aims to detect hallucinated content in LLM-generated text.
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Updated
Jul 6, 2026 - Python
[UAI 2026 Oral] SeSE: Black-Box Uncertainty Quantification for Large Language Models Based on Structural Information Theory, which aims to detect hallucinated content in LLM-generated text.
A complete-state structural framework investigating how one continuous reality produces observable structure across physical, biological, informational, and measurement domains.
Unsupervised social bot detection on multi-relational graphs via structural-entropy community detection
Implementation of "Entropic Flow Network for Bayesian Network Structure Learning," accepted at IEEE ICDM 2026.
Official implementation of GEBot: graph evidence for LLM-based social bot detection.
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