In Pursuit of the Trivial.

We compare the performance of state-of-the-art Large Language Models on a recently released benchmarking set for automated question answering for Icelandic and compare it with performance on questions from an Icelandic trivia game. We find that the models perform worse for questions on Icelandic sub...

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Publicado en:Digital Humanities in the Nordic & Baltic Countries Publications (DHNB Publications) Vol. 7; no. 2; pp. 1 - 9
Autores principales: Steingrímsson, Steinþór, Ármannsson, Bjarki
Formato: Artículo
Publicado: University of Oslo 2025
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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      pub: University of Oslo
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        atl: In Pursuit of the Trivial.
      aug:
        au:
          Steingrímsson, Steinþór
          Ármannsson, Bjarki
        affil: The Árni Magnússon Institute for Icelandic Studies
      su:
        Trivia contests
        Question answering systems
        Language models
        Benchmark problems (Computer science)
        Computer performance
        Culture
        Cognitive ability
      sug:
        subj:
          Trivia contests
          Question answering systems
          Language models
          Benchmark problems (Computer science)
          Computer performance
          Culture
          Cognitive ability
      keyword:
        Automatic Question Answering
        Icelandic
        Large Language Models
      ab: We compare the performance of state-of-the-art Large Language Models on a recently released benchmarking set for automated question answering for Icelandic and compare it with performance on questions from an Icelandic trivia game. We find that the models perform worse for questions on Icelandic subjects, specifically Icelandic culture, but somewhat surprisingly do better on a trivia game for people than on the benchmark set meant for language models, built around data that the model has seen during training. We also call into question some aspects of the benchmarking set and discuss what playing trivia games can tell us - if anything - about the capabilities of these models.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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