Time Awareness in Large Language Models: Benchmarking Fact Recall Across Time
<|reference_start|>Time Awareness in Large Language Models: Benchmarking Fact Recall Across Time: 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.<|reference_end|>
@article{herel2024time,
    title={Time Awareness in Large Language Models: Benchmarking Fact Recall Across
  Time},
    author={David Herel, Vojtech Bartek, Tomas Mikolov},
    journal={arXiv preprint arXiv:2409.13338},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.13338},
    primaryClass={cs.CL cs.AI}
}