Token-Level Likelihood-Array Regression for Membership Inference and AI-Generated Text Detection
Membership inference asks whether a text was used to train a language model, whereas AI-generated text detection asks whether it was generated by a language model rather than written by a human. Existing likelihood-based methods typically compress token-level probabilities into a few prespecified scores, most often using only probabilities conditioned on the full preceding context. We propose likelihood-array regression (LAR), which evaluates each target token under nested left-context windows and organizes the resulting likelihood-derived features into a structured array. After aligning arrays across texts of different lengths, LAR learns how detection information varies with context sca...
| Authors | Jiajun Sun, Zhanrui Cai |
|---|---|
| Primary category | stat.ML |
| Published | 23 August 2026 |
| Last updated at source | 23 August 2026 |
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