Benchmarking Temporal Reasoning and Alignment Across Chinese Dynasties

Zhenglin Wang, Jialong Wu, Pengfei LI, Yong Jiang, Deyu Zhou
2/24/2025
cs.CL

No official code found. Best implementation: Linking-ai/ctm_bench (6 stars, MIT, last commit 2026-01-07).

Abstract

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.

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MIT

(EACL26 Oral) Benchmarking Temporal Reasoning and Alignment Across Chinese Dynasties

60PythonFeb 24, 20259 months agoMIT

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Cite this paper

@article{wang2025benchmarking,
  title  = {Benchmarking Temporal Reasoning and Alignment Across Chinese Dynasties},
  author = {Zhenglin Wang and Jialong Wu and Pengfei LI and Yong Jiang and Deyu Zhou},
  year   = {2025},
  eprint = {2502.16922},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2502.16922v1}
}

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