Papers › DateLogicQA: Benchmarking Temporal Biases in Large Language Models
DateLogicQA: Benchmarking Temporal Biases in Large Language Models
Gagan Bhatia, MingZe Tang, Cristina Mahanta, Madiha Kazi
This paper introduces DateLogicQA, a benchmark with 190 questions covering diverse date formats, temporal contexts, and reasoning types. We propose the Semantic Integrity Metric to assess tokenization quality and analyse two biases: Representation-Level Bias, affecting embeddings, and Logical-Level Bias, influencing reasoning outputs. Our findings provide a comprehensive evaluation of LLMs' capabilities and limitations in temporal reasoning, highlighting key challenges in handling temporal data accurately.
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