  Meaning of words constantly changes given the events in modern civilization.
Large Language Models use word embeddings, which are often static and thus
cannot cope with this semantic change. Thus,it is important to resolve
ambiguity in word meanings. This paper is an effort in this direction, where we
explore methods for word sense disambiguation for the EvoNLP shared task. We
conduct rigorous ablations for two solutions to this problem. We see that an
approach using time-aware language models helps this task. Furthermore, we
explore possible future directions to this problem.
