Calibrating Post-Training Feature Shifts for LLM Data Contamination Detection
Large language models (LLMs) are trained on massive and largely undisclosed corpora that may contain copyrighted or privacy-sensitive content. Data contamination detection (DCD) therefore aims to determine whether a given text is a member of the pre-training corpus of a target LLM. Recent state-of-the-art DCD methods follow a feature-based paradigm that derives membership features from the input text and the corresponding model output. However, most modern LLMs undergo post-training, such as instruction tuning, preference optimization, and reasoning-oriented training, which can alter model outputs and shift the corresponding membership features, thereby reducing the separability between m...
| Authors | Zhen Yang, Mengqi Wang, Gengda Zhao et al. |
|---|---|
| Primary category | cs.CL |
| Published | 11 August 2026 |
| Last updated at source | 11 August 2026 |
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