Is Your LLM Outdated? A Deep Look at Temporal Generalization (2025)

Abstract: The rapid advancement of Large Language Models (LLMs) has led to the development of benchmarks that consider temporal dynamics, however, there remains a gap in understanding how well these models can generalize across temporal contexts due to the inherent dynamic nature of language and information. This paper introduces the concept of temporal generalization in LLMs, including bias in past and future generalizations. Then we introduce FreshBench, a new evaluation framework that employs fresh text and event prediction for assessing LLMs’ temporal adaptability, ensuring the evaluation process free from data leakage and subjective bias. The experiment shows significant temporal biases and a decline in performance over time.

## introduction proprietary large language models (llms) ope-nai (2023); googleai (2023); @xcite ; @xcite has been paralleled by efforts in the open-source community to democratize llms. meanwhile, the rapid advancement of llm democratization @xcite emphasizes the necessity for dynamic and robust benchmarks that accurately reflect their evolving capabilities. traditional benchmarks, broadly classified into knowledge-based assessments and opendialogue evaluations, each present unique challenges. knowledge-based assessments, exemplified by @xcite and c-eval @xcite , are prone to data manipulation, raising concerns about their real-world * benyou is the corresponding author. given the limitations of traditional methods, researchers are increasingly developing new approaches to evaluate models in diverse and dynamic contexts. for instance, recent efforts have introduced methods that assess models' abilities to handle temporal evolution, contextual understanding, and real-time information updates @xcite ; @xcite ; @xcite ; @xcite ; zhang and choi (2021); @xcite . while methods like realtimeqa and freshqa focus on continuously updating knowledge for real-time processing @xcite ; @xcite , others like latesteval @xcite emphasize recent text-based comprehension. in contrast, our work shifts the focus to evaluating models' world understanding through prediction, using a fact-based approach that minimizes bias and ensures objectivity in assessment. this method enhances robustness against data contamination and focuses on the model's capacity for accurate, real-world event prediction. we believe that the evaluation of llms should be conducted in scenarios that are resistant to hacking manipulation, with results that are objective and free from evaluator biases @xcite . additionally, time as a dynamic dimension, can also ensure robust and relevant evaluations. this paper explores an alternative way to evaluate large language models, i.e., on temporal generalization where time is considered as an important aspect. this approach significantly reduces the issue of data contamination by limiting access to future content. moreover, a retrospective perspective on time could be beneficial for temporal bias analysis for llms. to achieve this goal, we design two scenarios. the first is to have the model generate text based on recent data such as arxiv papers, news articles, new books, and wikipedia content. an excellent model should be able to generalize these new texts with high language likelihood without data leakage. the second scenario focuses on future event prediction. an excellent model should be able to understand the current context, integrate world knowledge, and effectively generalize to future scenarios. in these two scenarios, temporal generalization evaluation has two main advantages: eliminating the possibility of data leakage through temporal boundaries and enabling objective evaluation results without relying on subjective interpretations. our analysis indicates that using validation loss of new text to evaluate models is mostly effective. however, we observe that while performance on language likelihood is a strong indicator of a model's linguistic capabilities, it does not always align with specific abilities measured by existing benchmarks, which may represent discrete knowledge or other capacities that are important but not directly related to language modeling. our contributions are as follows: 1) we define and quantify temporal generalization and bias, establishing a basis for understanding and assessing the capabilities of llms over time. 2) we proposed a benchmark freshbench to test the capabilities of llms in predicting future events. this benchmark is dynamically updated to reflect the latest data, ensuring that our evaluations remain relevant and accurate in the face of rapidly evolving data environments. it experimentally assesses the performance of existing llms across various timeframes, providing insights into their historical and ongoing performance stability and adaptability. ## conceptualization of temporal generalization in this section, we will first conceptualize temporal generalization, then temporal biases and temporal degeneration in sec. 2.1. we will discuss ways to measure /textittemporal biases and degeneration in 2.2. ## temporal generalization through the lens of compression intelligence this section discusses how various models handle data from different periods, showcasing their performance stability or volatility. ## temporal generalization through the lens of future event prediction advantages of future event prediction over compression intelligence compression intelligence is limited by its inapplicability to closedsource models, where key indicators like logits or losses are inaccessible. this exclusion narrows comparative analysis and hinders a comprehensive understanding of the language technology landscape. additionally, language likelihood can be biased by models finely tuned to specific formats or domains, skewing overall evaluation and presenting a distorted view of a model's general performance. this motivated us to research temporal generalization and bias from the perspective of future prediction. in this section, we investigate temporal generalization (in sec. 4.1) and biases (sec. 4.2) through the lens of future event prediction. ## conclusion and community call to action we rigorously define and quantify temporal generalization and bias within llms, uncovering significant challenges in predicting and adapting to future contexts. our experiments reveal that llms often struggle with temporal biases, affecting reliability in dynamic scenarios requiring accurate forecasting. the evaluation framework and datasets we introduce allow for an in-depth assessment of model performance over time. the findings highlight shortcomings in existing benchmarks related to temporal shifts and underscore the urgent need for improvements in llm training and updating processes to enhance adaptability and mitigate biases. specifically, we urge the community to take note of this phenomenon: models tend to better comprehend earlier events. @xcite suggests that models are most familiar with knowledge up to the year 2020. however, our findings indicate that the understanding of world events (even in cases where models can remember) lingers around 2015, with a trend towards even earlier years (beyond the time range of our test data). this observation is contrary to our initial hypothesis that models may overfit current events, which is significantly more concerning. it suggests that caution is needed, especially for those hoping to use model judgments directly.