The Paper List on Data Contamination for Large Language Models Evaluation.

This python code performs an efficient speech reverberation starting from a dataset of close-talking speech signals and a collection of acoustic impulse responses.

Python .pyc decompiler (3.0–3.14) with a contamination-aware benchmark harness. Rule-only pass + one Codex call per module; evaluated on fuzz-synthetic (LLM-naïve) and *-obf (anonymised) corpora to put a number on the memorisation share. Three independent PyPI packages: pychd, pychd-pyfuzz, pychd-pyobf.

The official implementation of the paper "Data Contamination Calibration for Black-box LLMs" (ACL 2024)

The official repository for the paper entitled "Time Travel in LLMs: Tracing Data Contamination in Large Language Models."

DICE: Detecting In-distribution Data Contamination with LLM's Internal State

Python package developed to evaluate textual overlap (N-Grams) between two volumes of text.

The official repository for the paper entitled "Data Contamination Quiz: A Tool to Detect and Estimate Contamination in Large Language Models."

Zero-CoT Probe (ZCP) for detecting evasive data contamination in LLMs. Paper Link: http://arxiv.org/abs/2605.21856

Local contamination checks for eval data overlap, hashes, and n-gram leakage.

Turn code repos and curated visual repos into contamination-checked training datasets for local fine-tuning.