Reveals hidden human-like spans in machine-generated texts that raise detection complexity and proposes a stacked enhancement framework with hard-EM optimization to improve detectors across LLMs.
Simllm: Detecting sentences generated by large language models using similarity between the generation and its re-generation,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CL 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
A multi-level framework that models local and global relations among token detection scores to improve machine-generated text detection with low overhead.
citing papers explorer
-
Hidden Human-Like Nature of Machine-Generated Texts: Theory and Detection Enhancement
Reveals hidden human-like spans in machine-generated texts that raise detection complexity and proposes a stacked enhancement framework with hard-EM optimization to improve detectors across LLMs.
-
Multi-Level Contextual Token Relation Modeling for Machine-Generated Text Detection
A multi-level framework that models local and global relations among token detection scores to improve machine-generated text detection with low overhead.