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    <abstract>In this paper, we propose ChronoBias, a novel benchmark for evaluating time-conditional group bias in the 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, 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 parametric knowledge bias–which influences group bias across all time intervals–we identify time-sensitivity bias as an additional factor after a model’s knowledge cutoff, accounting for much of the variation in 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 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.</abstract>
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%0 Conference Proceedings
%T ChronoBias: A Benchmark for Evaluating Temporal Group Bias in the Time-sensitive Knowledge of Large Language Models
%A Kim, Kyungmin
%A Choi, Youngbin
%A Kim, Hyounghun
%A Kim, Dongwoo
%A Park, Sangdon
%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 kim-etal-2025-chronobias
%X In this paper, we propose ChronoBias, a novel benchmark for evaluating time-conditional group bias in the 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, 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 parametric knowledge bias–which influences group bias across all time intervals–we identify time-sensitivity bias as an additional factor after a model’s knowledge cutoff, accounting for much of the variation in 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 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.
%U https://aclanthology.org/2025.findings-emnlp.405/
%P 7658-7693

Markdown (Informal)

[ChronoBias: A Benchmark for Evaluating Temporal Group Bias in the Time-sensitive Knowledge of Large Language Models](https://aclanthology.org/2025.findings-emnlp.405/) (Kim et al., Findings 2025)

• ChronoBias: A Benchmark for Evaluating Temporal Group Bias in the Time-sensitive Knowledge of Large Language Models (Kim et al., Findings 2025)

ACL

• Kyungmin Kim, Youngbin Choi, Hyounghun Kim, Dongwoo Kim, and Sangdon Park. 2025. ChronoBias: A Benchmark for Evaluating Temporal Group Bias in the Time-sensitive Knowledge of Large Language Models. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 7658–7693, Suzhou, China. Association for Computational Linguistics.