@inproceedings{di-buono-etal-2024-evaluating,
title = "Evaluating Large Language Models for Linguistic Linked Data Generation",
author = "di Buono, Maria Pia and
Spahiu, Blerina and
Barbu Mititelu, Verginica",
editor = "S{\'e}rasset, Gilles and
Oliveira, Hugo Gon{\c{c}}alo and
Oleskeviciene, Giedre Valunaite",
booktitle = "Proceedings of the Workshop on Deep Learning and Linked Data (DLnLD) @ LREC-COLING 2024",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.dlnld-1.6",
pages = "66--75",
abstract = "Large language models (LLMs) have revolutionized human-machine interaction with their ability to converse and perform various language tasks. This study investigates the potential of LLMs for knowledge formalization using well-defined vocabularies, specifically focusing on OntoLex-Lemon. As a preliminary exploration, we test four languages (English, Italian, Albanian, Romanian) and analyze the formalization quality of nine words with varying characteristics applying a multidimensional evaluation approach. While manual validation provided initial insights, it highlights the need for developing scalable evaluation methods for future large-scale experiments. This research aims to initiate a discussion on the potential and challenges of utilizing LLMs for knowledge formalization within the Semantic Web framework.",
}
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%0 Conference Proceedings
%T Evaluating Large Language Models for Linguistic Linked Data Generation
%A di Buono, Maria Pia
%A Spahiu, Blerina
%A Barbu Mititelu, Verginica
%Y Sérasset, Gilles
%Y Oliveira, Hugo Gonçalo
%Y Oleskeviciene, Giedre Valunaite
%S Proceedings of the Workshop on Deep Learning and Linked Data (DLnLD) @ LREC-COLING 2024
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F di-buono-etal-2024-evaluating
%X Large language models (LLMs) have revolutionized human-machine interaction with their ability to converse and perform various language tasks. This study investigates the potential of LLMs for knowledge formalization using well-defined vocabularies, specifically focusing on OntoLex-Lemon. As a preliminary exploration, we test four languages (English, Italian, Albanian, Romanian) and analyze the formalization quality of nine words with varying characteristics applying a multidimensional evaluation approach. While manual validation provided initial insights, it highlights the need for developing scalable evaluation methods for future large-scale experiments. This research aims to initiate a discussion on the potential and challenges of utilizing LLMs for knowledge formalization within the Semantic Web framework.
%U https://aclanthology.org/2024.dlnld-1.6
%P 66-75
Markdown (Informal)
[Evaluating Large Language Models for Linguistic Linked Data Generation](https://aclanthology.org/2024.dlnld-1.6) (di Buono et al., DLnLD-WS 2024)
ACL