<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="di-buono-etal-2024-evaluating">
    <titleInfo>
        <title>Evaluating Large Language Models for Linguistic Linked Data Generation</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Maria</namePart>
        <namePart type="given">Pia</namePart>
        <namePart type="family">di Buono</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Blerina</namePart>
        <namePart type="family">Spahiu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Verginica</namePart>
        <namePart type="family">Barbu Mititelu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2024-05</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Workshop on Deep Learning and Linked Data (DLnLD) @ LREC-COLING 2024</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Gilles</namePart>
            <namePart type="family">Sérasset</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Hugo</namePart>
            <namePart type="given">Gonçalo</namePart>
            <namePart type="family">Oliveira</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Giedre</namePart>
            <namePart type="given">Valunaite</namePart>
            <namePart type="family">Oleskeviciene</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>ELRA and ICCL</publisher>
            <place>
                <placeTerm type="text">Torino, Italia</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <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.</abstract>
    <identifier type="citekey">di-buono-etal-2024-evaluating</identifier>
    <location>
        <url>https://aclanthology.org/2024.dlnld-1.6</url>
    </location>
    <part>
        <date>2024-05</date>
        <extent unit="page">
            <start>66</start>
            <end>75</end>
        </extent>
    </part>
</mods>
</modsCollection>

%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)

• Evaluating Large Language Models for Linguistic Linked Data Generation (di Buono et al., DLnLD-WS 2024)

ACL

• Maria Pia di Buono, Blerina Spahiu, and Verginica Barbu Mititelu. 2024. Evaluating Large Language Models for Linguistic Linked Data Generation. In Proceedings of the Workshop on Deep Learning and Linked Data (DLnLD) @ LREC-COLING 2024, pages 66–75, Torino, Italia. ELRA and ICCL.