Recite, Reconstruct, Recollect: Memorization in LMs as a Multifaceted Phenomenon

Memorization in language models is typically treated as a homogenous phenomenon, neglecting the specifics of the memorized data. We instead model memorization as the effect of a set of complex factors that describe each sample and relate it to the model and corpus. To build intuition around these factors, we break memorization down into a taxonomy: recitation of highly duplicated sequences, reconstruction of inherently predictable sequences, and recollection of sequences that are neither. We demonstrate the usefulness of our taxonomy by using it to construct a predictive model for memorization. By analyzing dependencies and inspecting the weights of the predictive model, we find that different factors influence the likelihood of memorization differently depending on the taxonomic category.

@article{prashanth2025_2406.17746,
  title={ Recite, Reconstruct, Recollect: Memorization in LMs as a Multifaceted Phenomenon },
  author={ USVSN Sai Prashanth and Alvin Deng and Kyle O'Brien and Jyothir S V and Mohammad Aflah Khan and Jaydeep Borkar and Christopher A. Choquette-Choo and Jacob Ray Fuehne and Stella Biderman and Tracy Ke and Katherine Lee and Naomi Saphra },
  journal={arXiv preprint arXiv:2406.17746},
  year={ 2025 }
}