Will It Still Be True Tomorrow? Multilingual Evergreen Question
  Classification to Improve Trustworthy QA
Authors: name: Sergey Pletenev, name: Maria Marina, name: Nikolay Ivanov, name: Daria Galimzianova, name: Nikita Krayko, name: Mikhail Salnikov, name: Vasily Konovalov, name: Alexander Panchenko, name: Viktor Moskvoretskii

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.