@inproceedings{homburger-bar-2025-large,
    title = "Large Temporal Models: Unlocking Temporal Understanding in {LLM}s for Temporal Relation Classification",
    author = "Homburger, Omri  and
      Bar, Kfir",
    editor = "Inui, Kentaro  and
      Sakti, Sakriani  and
      Wang, Haofen  and
      Wong, Derek F.  and
      Bhattacharyya, Pushpak  and
      Banerjee, Biplab  and
      Ekbal, Asif  and
      Chakraborty, Tanmoy  and
      Singh, Dhirendra Pratap",
    booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.ijcnlp-long.117/",
    pages = "2156--2171",
    ISBN = "979-8-89176-298-5",
    abstract = "We present Large Temporal Model, a Large Language Model (LLM) that excels in Temporal Relation Classification (TRC). We show how a carefully designed fine-tuning strategy, using a novel two-step fine-tuning approach, can adapt LLMs for TRC. Our approach is focused on global TRC, enabling simultaneous classification of all temporal relations within a document. Unlike traditional pairwise methods, our approach performs global inference in a single step, improving both efficiency and consistency. Evaluations on the MATRES and OmniTemp benchmarks demonstrate that, for the first time, an LLM achieves state-of-the-art performance, outperforming previous pairwise and global TRC methods. Results show that our global approach produces more consistent and accurate temporal graphs. Ablation studies further validate the effectiveness of our two-step fine-tuning strategy, while analyses reveal why our approach succeeds in increasing performance and reducing inconsistencies."
}