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This paper introduces a novel task, transitive inference with exceptions, and analytically characterizes how neural network models (kernel ridge regression) balance relational generalization and memorization. The theory is validated in pretrained language models, showing systematic mistakes predicted by the theory.
This paper studies how fill-in-the-middle (FIM) pretraining affects verbatim memorization, finding that FIM more often recovers short spans while standard left-to-right training recovers long exact continuations, and that memorization under FIM grows linearly with repetitions.
This paper introduces Zero-CoT Probe (ZCP), a black-box detection method that identifies evasive data contamination in LLMs by truncating chain-of-thought reasoning and comparing performance on perturbed datasets, achieving robust detection of both direct and indirect contamination.
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