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    <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.</abstract>
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%0 Conference Proceedings
%T LexTime: A Benchmark for Temporal Ordering of Legal Events
%A Barale, Claire
%A Barrett, Leslie
%A Bajaj, Vikram Sunil
%A Rovatsos, Michael
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F barale-etal-2025-lextime
%X 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.
%R 10.18653/v1/2025.findings-emnlp.280
%U https://aclanthology.org/2025.findings-emnlp.280/
%U https://doi.org/10.18653/v1/2025.findings-emnlp.280
%P 5220-5236

Markdown (Informal)

[LexTime: A Benchmark for Temporal Ordering of Legal Events](https://aclanthology.org/2025.findings-emnlp.280/) (Barale et al., Findings 2025)

• LexTime: A Benchmark for Temporal Ordering of Legal Events (Barale et al., Findings 2025)

ACL

• Claire Barale, Leslie Barrett, Vikram Sunil Bajaj, and Michael Rovatsos. 2025. LexTime: A Benchmark for Temporal Ordering of Legal Events. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 5220–5236, Suzhou, China. Association for Computational Linguistics.