Evaluating LLMs at Evaluating Temporal Generalization

The rapid advancement of 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. Furthermore, these benchmarks do not adequately measure the models' capabilities over a broader temporal range or their adaptability over time. We examine current LLMs in terms of temporal generalization and bias, revealing that various temporal biases emerge in both language likelihood and prognostic prediction. This serves as a caution for LLM practitioners to pay closer attention to mitigating temporal biases. Also, we propose an evaluation framework Freshbench for dynamically generating benchmarks from the most recent real-world prognostication prediction. Our code is available at https://github.com/FreedomIntelligence/FreshBench. The dataset will be released soon.