Temporal reasoning is not the core bottleneck for LLMs on time-based QA; the real issue is unstructured text-to-event mapping, addressed by a neuro-symbolic system with PIS that reaches 100% accuracy on benchmarks when representations are correct.
Towards benchmarking and improving the temporal reasoning capability of large language models
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Late fusion of absolute and relative temporal metadata into Transformer NER models produces more robust performance than early fusion on French and German historical datasets, especially in early noisy periods.
citing papers explorer
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Temporal Reasoning Is Not the Bottleneck: A Probabilistic Inconsistency Framework for Neuro-Symbolic QA
Temporal reasoning is not the core bottleneck for LLMs on time-based QA; the real issue is unstructured text-to-event mapping, addressed by a neuro-symbolic system with PIS that reaches 100% accuracy on benchmarks when representations are correct.
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A Study of Temporal Fusion Strategies for Named Entity Recognition in Historical Texts
Late fusion of absolute and relative temporal metadata into Transformer NER models produces more robust performance than early fusion on French and German historical datasets, especially in early noisy periods.