@inproceedings{kang-etal-2025-trial,
    title = "{TRIAL}: Token Relations and Importance Aware Late-interaction for Accurate Text Retrieval",
    author = "Kang, Hyukkyu  and
      Kim, Injung  and
      Han, Wook-Shin",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.854/",
    pages = "16875--16888",
    ISBN = "979-8-89176-332-6",
    abstract = "Late-interaction based multi-vector retrieval systems have greatly advanced the field of information retrieval by enabling fast and accurate search over millions of documents. However, these systems rely on a naive summation of token-level similarity scores which often leads to inaccurate relevance estimation caused by the tokenization of semantic units (e.g., words and phrases) and the influence of low-content words (e.g., articles and prepositions). To address these challenges, we propose **TRIAL**: **T**oken **R**elations and **I**mportance **A**ware **L**ate-interaction, which enhances late interaction by explicitly modeling token relations and token importance in relevance scoring. Extensive experiments on three widely used benchmarks show that TRIAL achieves state-of-the-art accuracy, with an nDCG@10 of 46.3 on MSMARCO (in-domain), and average nDCG@10 scores of 51.09 and 72.15 on BEIR and LoTTE Search (out-of-domain), respectively. With superior accuracy, TRIAL maintains competitive retrieval speed compared to existing late-interaction methods, making it a practical solution for large-scale text retrieval."
}