Luminol-AIDetect detects machine-generated text zero-shot by extracting perplexity-based features from original and shuffled text versions, using density estimation and ensemble prediction to exploit greater structural fragility in AI output.
Title resolution pending
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
fields
cs.CL 4years
2026 4representative citing papers
A forecasting-based metric called 100-Endings quantifies story tension via prediction mismatches, correctly ranks human stories above AI ones, and supports a structural pipeline for higher-tension LLM story generation.
IRM derives implicit reward signals from off-the-shelf LLMs to detect generated text zero-shot and reports better results than prior zero-shot and supervised detectors on the DetectRL benchmark.
citing papers explorer
-
Luminol-AIDetect: Fast Zero-shot Machine-Generated Text Detection based on Perplexity under Text Shuffling
Luminol-AIDetect detects machine-generated text zero-shot by extracting perplexity-based features from original and shuffled text versions, using density estimation and ensemble prediction to exploit greater structural fragility in AI output.
-
Spoiler Alert: Narrative Forecasting as a Metric for Tension in LLM Storytelling
A forecasting-based metric called 100-Endings quantifies story tension via prediction mismatches, correctly ranks human stories above AI ones, and supports a structural pipeline for higher-tension LLM story generation.
-
Zero-Shot Detection of LLM-Generated Text via Implicit Reward Model
IRM derives implicit reward signals from off-the-shelf LLMs to detect generated text zero-shot and reports better results than prior zero-shot and supervised detectors on the DetectRL benchmark.
- How Human-Like Are Large Language Models? A Register-Aware Linguistic Evaluation Framework