On the Robustness of Temporal Factual Knowledge in Language Models

This paper explores the temporal robustness of language models (LMs) in handling factual knowledge. While LMs can often complete simple factual statements, their ability to manage temporal facts (those valid only within specific timeframes) remains uncertain. We design a controlled experiment to test the robustness of temporal factual knowledge inside LMs, which we use to evaluate several pretrained and instruction-tuned models using prompts on popular Wikidata facts, assessing their performance across different temporal granularities (Day, Month, and Year). Our findings indicate that even very large state-of-the-art models, such as Llama-3.1-70B, vastly lack robust knowledge of temporal facts. In addition, they are incapable of generalizing their knowledge from one granularity to another. These results highlight the inherent limitations of using LMs as temporal knowledge bases. The source code and data to reproduce our experiments will be released.