Discourse-Aware In-Context Learning for Temporal Expression
  Normalization
Akash Kumar Gautam Lukas Lange Jannik Strötgen
Temporal expression (TE) normalization is a well-studied problem. However,
the predominately used rule-based systems are highly restricted to specific
settings, and upcoming machine learning approaches suffer from a lack of
labeled data. In this work, we explore the feasibility of proprietary and
open-source large language models (LLMs) for TE normalization using in-context
learning to inject task, document, and example information into the model. We
explore various sample selection strategies to retrieve the most relevant set
of examples. By using a window-based prompt design approach, we can perform TE
normalization across sentences, while leveraging the LLM knowledge without
training the model. Our experiments show competitive results to models designed
for this task. In particular, our method achieves large performance
improvements for non-standard settings by dynamically including relevant
examples during inference.