@inproceedings{kim-etal-2025-chronobias,
    title = "{C}hrono{B}ias: A Benchmark for Evaluating Temporal Group Bias in the Time-sensitive Knowledge of Large Language Models",
    author = "Kim, Kyungmin  and
      Choi, Youngbin  and
      Kim, Hyounghun  and
      Kim, Dongwoo  and
      Park, Sangdon",
    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/author-page-yu-wang-polytechnic/2025.findings-emnlp.405/",
    doi = "10.18653/v1/2025.findings-emnlp.405",
    pages = "7658--7693",
    ISBN = "979-8-89176-335-7",
    abstract = "In this paper, we propose $\texttt{ChronoBias}$, a novel benchmark for evaluating $\textit{time-conditional group bias}$ in the $\textit{time-sensitive}$ knowledge of large language models (LLMs).Our benchmark is constructed via a template-based semi-automated generation method, balancing the quality-quantity trade-off in existing benchmark curation approaches.For knowledge that changes over time, $\textit{time-conditional group bias}$ exhibits varying patterns across time intervals, evident in both the best- and worst-performing groups and in the bias metric itself.In addition to $\textit{parametric knowledge bias}${--}which influences group bias across all time intervals{--}we identify $\textit{time-sensitivity bias}$ as an additional factor after a model{'}s knowledge cutoff, accounting for much of the variation in $\textit{time-conditional group bias}$ over time.Since both biases are irreducible, retrieval-augmented generation (RAG) can be a promising approach, as it can address post-cutoff knowledge and better leverage pretraining knowledge that is underrepresented in the model parameters.While RAG improves both overall performance and group bias, we observe that the disparate patterns of $\textit{time-conditional group bias}$ still persist.Therefore, through extensive experiments with various model configurations, we illustrate how accurate and fair RAG-based LLMs should behave and provide actionable guidelines toward constructing such ideal models."
}