  Language models (LMs) are trained on web text originating from many points in
time and, in general, without any explicit temporal grounding. This work
investigates the temporal chaos of pretrained LMs and explores various methods
to align their internal knowledge to a target time, which we call "temporal
alignment." To do this, we first automatically construct a dataset containing
20K time-sensitive questions and their answers for each year from 2000 to 2023.
Based on this dataset, we empirically show that pretrained LMs (e.g., LLaMa2),
despite having a recent pretraining cutoff (e.g., 2022), mostly answer
questions using earlier knowledge (e.g., in 2019). We then develop several
methods, from prompting to finetuning, to align LMs to use their most recent
knowledge when answering questions, and investigate various factors in this
alignment. Our experiments demonstrate that aligning LLaMa2 to the year 2022
can enhance its performance by up to 62% according to that year's answers. This
improvement occurs even without explicitly mentioning time information,
indicating the possibility of aligning models' internal sense of time after
pretraining. Finally, we find that alignment to a historical time is also
possible, with up to 2.8$\times$ the performance of the unaligned LM in 2010 if
finetuning models to that year. These findings hint at the sophistication of
LMs' internal knowledge organization and the necessity of tuning them properly.
