@inproceedings{barale-etal-2025-lextime,
    title = "{L}ex{T}ime: A Benchmark for Temporal Ordering of Legal Events",
    author = "Barale, Claire  and
      Barrett, Leslie  and
      Bajaj, Vikram Sunil  and
      Rovatsos, Michael",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/name-variant-enfa-fane/2025.findings-emnlp.280/",
    doi = "10.18653/v1/2025.findings-emnlp.280",
    pages = "5220--5236",
    ISBN = "979-8-89176-335-7",
    abstract = "Understanding temporal relationships and accurately reconstructing the event timeline is important for case law analysis, compliance monitoring, and legal summarization. However, existing benchmarks lack specialized language evaluation, leaving a gap in understanding how LLMs handle event ordering in legal contexts. We introduce LexTime, a dataset designed to evaluate LLMs' event ordering capabilities in legal language, consisting of 512 instances from U.S. Federal Complaints with annotated event pairs and their temporal relations. Our findings show that (1) LLMs are more accurate on legal event ordering than on narrative texts (up to +10.5{\%}); (2) longer input contexts and implicit events boost accuracy, reaching 80.8{\%} for implicit-explicit event pairs; (3) legal linguistic complexities and nested clauses remain a challenge. While performance is promising, specific features of legal texts remain a bottleneck for legal temporal event reasoning, and we propose concrete modeling directions to better address them."
}