Will It Still Be True Tomorrow? Multilingual Evergreen Question Classification to Improve Trustworthy QA

Sergey Pletenev, Maria Marina, Ivanov, daria galimzianova, Krayko Nikita, Mikhail Salnikov, Vasily Konovalov, Alexander Panchenko, Viktor Moskvoretskii

Will It Still Be True Tomorrow? Multilingual Evergreen Question Classification to Improve Trustworthy QA: 143 upvotes on Hugging Face Daily Papers, #1 of 24 papers on 2025-06-09. Day-by-day upvote history.

Large Language Models (LLMs) often hallucinate in question answering (QA) tasks. A key yet underexplored factor contributing to this is the temporality of questions -- whether they are evergreen (answers remain stable over time) or mutable (answers change). In this work, we introduce EverGreenQA, the first multilingual QA dataset with evergreen labels, supporting both evaluation and training. Using EverGreenQA, we benchmark 12 modern LLMs to assess whether they encode question temporality explicitly (via verbalized judgments) or implicitly (via uncertainty signals). We also train EG-E5, a lightweight multilingual classifier that achieves SoTA performance on this task. Finally, we demonstrate the practical utility of evergreen classification across three applications: improving self-knowledge estimation, filtering QA datasets, and explaining GPT-4o retrieval behavior.

Paper page on Hugging Face · arXiv

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