Discourse-Aware In-Context Learning for Temporal Expression Normalization

arXiv:2404.07775v1 Announce Type: cross
Abstract: 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 …

abstract arxiv context cs.ai cs.cl cs.lg data discourse explore however in-context learning language language models large language large language models llms machine machine learning normalization proprietary systems temporal type work