The Unreasonable Ineffectiveness of Nucleus Sampling on Mitigating Text Memorization

We present OpenMemText a diagnostic dataset with a known distribution of duplicates that gives us some control over the likelihood of memorization of certain parts of the training data. Given this diagnostic dataset, we analyse the text memorization behavior of large language models (LLMs) when subjected to nucleus sampling. Stochastic decoding methods like nucleus sampling are typically applied to overcome issues such as monotonous and repetitive text generation, which are often observed with maximization-based decoding techniques. We hypothesize that nucleus sampling might also reduce the occurrence of memorization patterns, because it could lead to the selection of tokens outside the memorized sequence. See https://github.com/lukaborec/memorization-nucleus-sampling for more information.