  We present time vectors, a simple tool to customize language models to new
time periods. Time vectors are created by finetuning a language model on data
from a single time (e.g., a year or month), and then subtracting the weights of
the original pretrained model. This vector specifies a direction in weight
space that, as our experiments show, improves performance on text from that
time period. Time vectors specialized to adjacent time periods appear to be
positioned closer together in a manifold. Using this structure, we interpolate
between time vectors to induce new models that perform better on intervening
and future time periods, without any additional training. We demonstrate the
consistency of our findings across different tasks, domains, model sizes, and
time scales. Our results suggest that time is encoded in the weight space of
finetuned models.
