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arXiv · 2412.13377

DateLogicQA: Benchmarking Temporal Biases in Large Language Models

Gagan Bhatia·MingZe Tang·Cristina Mahanta·Madiha Kazi
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Abstract

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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Gagan Bhatia, MingZe Tang, Cristina Mahanta, Madiha Kazi. 2024-12-17. DateLogicQA: Benchmarking Temporal Biases in Large Language Models. https://arxiv.org/abs/2412.13377

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