Exploring In-Context Learning Strategies for Temporal Ordering of Legal Events using Large Language Models.

Large Language Models (LLMs) are increasingly adopted for legal document understanding by attorneys and legal consultants. Despite advances in adapting LLMs to their legal terminology and domain-specific linguistic nuances, the LLMs’ ability to reason about temporal relations in legal documents remains largely underexplored. In this work, we explore the capabilities of LLMs to verify the correctness of a legal temporal ordering clause and to classify the type of temporal relationships between two legal entities. The results achieved on a public Englishwritten benchmark show that (1) instruction-based models generally perform better than the corresponding chat versions; (2) LLMs reasoning capabilities are, typically, marginally useful to address the specific temporal reasoning tasks; (3) LLMs under a Few-Shot Learning (FSL) setting turn out to be the most effective, with Grok 4 surpassing the state of the art.