Time Awareness in Large Language Models: Benchmarking Fact Recall Across Time

• URL: http://arxiv.org/abs/2409.13338v1
• Date: Fri, 20 Sep 2024 08:57:20 GMT
• Title: Time Awareness in Large Language Models: Benchmarking Fact Recall Across Time
• Authors: David Herel, Vojtech Bartek, Tomas Mikolov,
• Abstract summary: We introduce a novel dataset designed to rigorously test large language models' ability to handle time-sensitive facts. Our benchmark offers a systematic way to measure how well LLMs align their knowledge with the correct time context.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Who is the US President? The answer changes depending on when the question is asked. While large language models (LLMs) are evaluated on various reasoning tasks, they often miss a crucial dimension: time. In real-world scenarios, the correctness of answers is frequently tied to temporal context. In this paper, we introduce a novel dataset designed to rigorously test LLMs' ability to handle time-sensitive facts. Our benchmark offers a systematic way to measure how well LLMs align their knowledge with the correct time context, filling a key gap in current evaluation methods and offering a valuable tool for improving real-world applicability in future models.

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