@inproceedings{giarelis-etal-2024-unified,
    title = "A Unified {LLM}-{KG} Framework to Assist Fact-Checking in Public Deliberation",
    author = "Giarelis, Nikolaos  and
      Mastrokostas, Charalampos  and
      Karacapilidis, Nikos",
    editor = "Hautli-Janisz, Annette  and
      Lapesa, Gabriella  and
      Anastasiou, Lucas  and
      Gold, Valentin  and
      Liddo, Anna De  and
      Reed, Chris",
    booktitle = "Proceedings of the First Workshop on Language-driven Deliberation Technology (DELITE) @ LREC-COLING 2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.delite-1.2",
    pages = "13--19",
    abstract = "Fact-checking plays a crucial role in public deliberation by promoting transparency, accuracy, credibility, and accountability. Aiming to augment the efficiency and adoption of current public deliberation platforms, which mostly rely on the abilities of participants to meaningfully process and interpret the associated content, this paper explores the combination of deep learning and symbolic reasoning. Specifically, it proposes a framework that unifies the capabilities of Large Language Models (LLMs) and Knowledge Graphs (KGs), and reports on an experimental evaluation. This evaluation is conducted through a questionnaire asking users to assess a baseline LLM against the proposed framework, using a series of fact-checking metrics, namely readability, coverage, non-redundancy, and quality. The experimentation results are promising and confirm the potential of combining the capabilities of these two technologies in the context of public deliberation and digital democracy.",
}

Fact-checking plays a crucial role in public deliberation by promoting transparency, accuracy, credibility, and accountability. Aiming to augment the efficiency and adoption of current public deliberation platforms, which mostly rely on the abilities of participants to meaningfully process and interpret the associated content, this paper explores the combination of deep learning and symbolic reasoning. Specifically, it proposes a framework that unifies the capabilities of Large Language Models (LLMs) and Knowledge Graphs (KGs), and reports on an experimental evaluation. This evaluation is conducted through a questionnaire asking users to assess a baseline LLM against the proposed framework, using a series of fact-checking metrics, namely readability, coverage, non-redundancy, and quality. The experimentation results are promising and confirm the potential of combining the capabilities of these two technologies in the context of public deliberation and digital democracy.
Giarelis, Nikolaos Mastrokostas, Charalampos Karacapilidis, Nikos
A Unified LLM-KG Framework to Assist Fact-Checking in Public Deliberation