Will It Still Be True Tomorrow? Multilingual Evergreen Question
  Classification to Improve Trustworthy QA
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.
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Sergey Pletenev
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2025-06-09T10:12:55.604Z
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Sergey Pletenev
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memyprokotow
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Maria Marina
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2025-06-09T10:12:59.278Z
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Maria Marina
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zlatamaria
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Nikolay Ivanov
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2025-06-02T07:48:42.811Z
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Ivanov
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VirVen
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Daria Galimzianova
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2025-06-10T09:29:46.228Z
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daria galimzianova
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daria0gal
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Nikita Krayko
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2025-06-09T10:12:53.523Z
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Krayko Nikita
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nakrayko
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Mikhail Salnikov
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2025-06-10T11:21:01.556Z
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Mikhail Salnikov
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msalnikov
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Vasily Konovalov
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2025-06-10T09:29:43.873Z
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Vasily Konovalov
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Vasily
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Alexander Panchenko
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Viktor Moskvoretskii
claimed_verified
2025-06-09T10:12:57.325Z
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Viktor Moskvoretskii
user
VityaVitalich
Will It Still Be True Tomorrow? Multilingual Evergreen Question
  Classification to Improve Trustworthy QA
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.
EverGreenQA, a multilingual QA dataset with evergreen labels, is introduced to benchmark LLMs on temporality encoding and assess their performance through verbalized judgments and uncertainty signals.
Large Language Models QA evergreen mutable temporality Multilingual QA dataset EG-E5 lightweight multilingual classifier SoTA performance self-knowledge estimation filtering QA datasets GPT-4o retrieval behavior