Temporal reasoning is fundamental to human cognition and is crucial for various real-world applications. While recent advances in Large Language Models have demonstrated promising capabilities in temporal reasoning, existing benchmarks primarily rely on rule-based construction, lack contextual depth, and involve a limited range of temporal entities. To address these limitations, we introduce Chinese Time Reasoning (CTM), a benchmark designed to evaluate LLMs on temporal reasoning within the extensive scope of Chinese dynastic chronology. CTM emphasizes cross-entity relationships, pairwise temporal alignment, and contextualized and culturally-grounded reasoning, providing a comprehensive evaluation. Extensive experimental results reveal the challenges posed by CTM and highlight potential avenues for improvement.
@article{arxiv.2502.16922,
title = {Benchmarking Temporal Reasoning and Alignment Across Chinese Dynasties},
author = {Zhenglin Wang and Jialong Wu and Pengfei LI and Yong Jiang and Deyu Zhou},
journal= {arXiv preprint arXiv:2502.16922},
year = {2025}
}