@inproceedings{graciotti-etal-2024-latent,
    title = "Latent vs Explicit Knowledge Representation: How {C}hat{GPT} Answers Questions about Low-Frequency Entities",
    author = "Graciotti, Arianna  and
      Presutti, Valentina  and
      Tripodi, Rocco",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.888",
    pages = "10172--10185",
    abstract = "In this paper, we present an evaluation of two different approaches to the free-form Question Answering (QA) task. The main difference between the two approaches is that one is based on latent representations of knowledge, and the other uses explicit knowledge representation. For the evaluation, we developed DynaKnowledge, a new benchmark composed of questions concerning Wikipedia low-frequency entities. We wanted to ensure, on the one hand, that the questions are answerable and, on the other, that the models can provide information about very specific facts. The evaluation that we conducted highlights that the proposed benchmark is particularly challenging. The best model answers correctly only on 50{\%} of the questions. Analysing the results, we also found that ChatGPT shows low reliance on low-frequency entity questions, manifesting a popularity bias. On the other hand, a simpler model based on explicit knowledge is less affected by this bias. With this paper, we want to provide a living benchmark for open-form QA to test knowledge and latent representation models on a dynamic benchmark.",
}

In this paper, we present an evaluation of two different approaches to the free-form Question Answering (QA) task. The main difference between the two approaches is that one is based on latent representations of knowledge, and the other uses explicit knowledge representation. For the evaluation, we developed DynaKnowledge, a new benchmark composed of questions concerning Wikipedia low-frequency entities. We wanted to ensure, on the one hand, that the questions are answerable and, on the other, that the models can provide information about very specific facts. The evaluation that we conducted highlights that the proposed benchmark is particularly challenging. The best model answers correctly only on 50{\%} of the questions. Analysing the results, we also found that ChatGPT shows low reliance on low-frequency entity questions, manifesting a popularity bias. On the other hand, a simpler model based on explicit knowledge is less affected by this bias. With this paper, we want to provide a living benchmark for open-form QA to test knowledge and latent representation models on a dynamic benchmark.
Graciotti, Arianna Presutti, Valentina Tripodi, Rocco
Latent vs Explicit Knowledge Representation: How ChatGPT Answers Questions about Low-Frequency Entities