How new data pollutes LLM knowledge and how to dilute it

Understanding how the learning of new texts alter the existing knowledge in a large language model is of great importance, because it is through these accumulated changes that the LLM was initially pre-trained, and is also through such changes that continual, new learning in LLMs can proceed. As a result, both desirable alterations (i.e. generalization) and undesirable alterations (i.e. hallucination) can occur. Here, we study the learning of new texts, one at a time, and ask: how does it impact the underlying LLM knowledge? We show that learning new texts induce 'priming', an undesirable effect that pollutes existing knowledge where it should not. Centrally, we demonstrate that we can predict how much priming will happen after learning, using token probability before learning. This was empirically robust across models (PALM-2-xs/s, Gemma-2b, Llama-2-7b), of various sizes, and training stages. To show this, we created a new dataset, called "Outlandish" consisting of 1320 different samples with diverse textual characteristics. Finally, we propose two strategies to mitigate the spread of priming: first, a simple text augmentation technique which we call the "stepping-stone'', and second, a novel update pruning technique ("ignore-k"). These decrease priming by a median of 50%-75% and 50%-95% respectively depending on the model architecture, and enhance the specificity of new learning in language models. The dataset and reproducible findings can be found [LINK omitted for double blind review].