Benchmarking Temporal Reasoning and Alignment Across Chinese Dynasties
This work addresses the need for more contextual and culturally-grounded temporal reasoning benchmarks for AI researchers, though it is incremental as it builds on existing benchmark efforts.
The authors tackled the limitations of existing temporal reasoning benchmarks by introducing the Chinese Time Reasoning (CTM) benchmark, which evaluates Large Language Models on temporal reasoning across Chinese dynasties, revealing challenges and improvement avenues.
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.