Robust Detection of LLM-Generated Text under Contamination
Read the original on arXiv Machine Learning →The paper investigates how to detect text generated by large language models (LLMs) when the data has been edited or contaminated. By modeling human and machine text as finite-order Markov processes with Huber contamination, the authors derive an exact boundary that determines when reliable detection is possible. They show that a clipped likelihood-ratio test can achieve vanishing worst‑case errors below this boundary and that clipping improves robustness across several detectors and datasets, yielding significant gains in true‑positive rates at a fixed false‑positive rate.
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