@inproceedings{liu-etal-2026-exante,
    title = "{E}x{A}nte: A Benchmark for Ex-Ante Inference in Large Language Models",
    author = "Liu, Yachuan  and
      Wei, Xiaochun  and
      Shi, Lin  and
      Li, Xinnuo  and
      Zhang, Bohan  and
      Dhillon, Paramveer  and
      Mei, Qiaozhu",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-long.72/",
    pages = "1551--1571",
    ISBN = "979-8-89176-380-7",
    abstract = "Large language models (LLMs) struggle with ex-ante reasoning{---}making inferences or predictions without access to future information. Even under explicit temporal cutoffs, they often rely on internalized post-cutoff knowledge. To systematically evaluate this issue, we introduce a benchmark that assesses LLMs' ex-ante inference ability across four tasks: stock prediction, question answering, Wikipedia event generation, and scientific publication generation. We quantify temporal leakage using a leakage rate metric, which measures models' reliance on future information beyond cutoff timestamps, and a quality measure that evaluates task performance. Experimental results show that LLMs frequently violate temporal constraints across tasks, revealing persistent challenges in ex-ante reasoning. Our benchmark serves as a rigorous testbed for studying temporal reasoning in time-sensitive contexts and provides complete datasets, results, and evaluation resources to support future research on improving temporal consistency in modern LLMs."
}