DateLogicQA: Benchmarking Temporal Biases in Large Language Models

• URL: http://arxiv.org/abs/2412.13377v1
• Date: Tue, 17 Dec 2024 23:25:47 GMT
• Title: DateLogicQA: Benchmarking Temporal Biases in Large Language Models
• Authors: Gagan Bhatia, MingZe Tang, Cristina Mahanta, Madiha Kazi,
• Abstract summary: 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.
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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. The GitHub repository for our work is available at https://github.com/gagan3012/EAIS-Temporal-Bias

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