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cs.CL2026
Log-Likelihood, Simpson's Paradox, and the Detection of Machine-Generated Text
Tom Kempton, Viktor Drobnyi, Maeve Madigan +1
The ability to reliably distinguish human-written text from that generated by large language models is of profound societal importance. The dominant approach to this problem exploi…
cs.CL2025
TempTest: Local Normalization Distortion and the Detection of Machine-generated Text
Tom Kempton, Stuart Burrell, Connor Cheverall
Existing methods for the zero-shot detection of machine-generated text are dominated by three statistical quantities: log-likelihood, log-rank, and entropy. As language models mimi…
cs.CL2025
Local Normalization Distortion and the Thermodynamic Formalism of Decoding Strategies for Large Language Models
Tom Kempton, Stuart Burrell
Advances in hardware and language model architecture have spurred a revolution in natural language generation. However, autoregressive models compute probability distributions over…