Is Your LLM Outdated? Evaluating LLMs at Temporal Generalization

• URL: http://arxiv.org/abs/2405.08460v2
• Date: Wed, 10 Jul 2024 17:57:01 GMT
• Title: Is Your LLM Outdated? Evaluating LLMs at Temporal Generalization
• Authors: Chenghao Zhu, Nuo Chen, Yufei Gao, Yunyi Zhang, Prayag Tiwari, Benyou Wang,
• Abstract summary: The rapid advancement of Large Language Models (LLMs) highlights the urgent need for evolving evaluation methodologies. Traditional benchmarks, which are often static, fail to capture the continually changing information landscape. Our study examines temporal generalization, which includes the ability to understand, predict, and generate text relevant to past, present, and future contexts.
• Score: 37.58752947129519
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rapid advancement of Large Language Models (LLMs) highlights the urgent need for evolving evaluation methodologies that keep pace with improvements in language comprehension and information processing. However, traditional benchmarks, which are often static, fail to capture the continually changing information landscape, leading to a disparity between the perceived and actual effectiveness of LLMs in ever-changing real-world scenarios. Our study examines temporal generalization, which includes the ability to understand, predict, and generate text relevant to past, present, and future contexts, revealing significant temporal biases in LLMs. We propose an evaluation framework, for dynamically generating benchmarks from recent real-world predictions. Experiments demonstrate that LLMs struggle with temporal generalization, showing performance decline over time. These findings highlight the necessity for improved training and updating processes to enhance adaptability and reduce biases. Our code, dataset and benchmark are available at https://github.com/FreedomIntelligence/FreshBench.

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