{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T11:20:44Z","timestamp":1765279244068,"version":"3.40.5"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"type":"electronic","value":"9781643685373"}],"license":[{"start":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T00:00:00Z","timestamp":1726012800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,9,11]]},"abstract":"<jats:p>Purpose: This study investigates the verbalization of answers generated by knowledge graph question answering (KGQA) systems using large language models. In user-centric applications, such as dialogue systems and voice assistants, answer verbalization is an essential step to enhance the quality of interactions. Methodology: We experimented with different large language models to verbalize answers from knowledge-based question-answering systems. In particular, we fine-tuned the LLM models (T5, BART and PEGASUS) on different inputs, including SPARQL queries and triples, to determine which model performs best for answer verbalization. Findings: We found that fine-tuning language models and introducing additional knowledge such as SPARQL queries, achieve state-of-the-art results in verbalizing answers from KGQA systems. Value: Our approach can be used to generate answers verbalization for different KGQA systems, including dialogue systems or voice assistants.<\/jats:p>","DOI":"10.3233\/ssw240027","type":"book-chapter","created":{"date-parts":[[2024,9,16]],"date-time":"2024-09-16T07:46:30Z","timestamp":1726472790000},"source":"Crossref","is-referenced-by-count":1,"title":["Enhancing Answers Verbalization Using Large Language Models"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5324-4952","authenticated-orcid":false,"given":"Daniel","family":"Vollmers","sequence":"first","affiliation":[{"name":"Data Science Group, Paderborn University, Germany"}]},{"given":"Parth","family":"Sharma","sequence":"additional","affiliation":[{"name":"Data Science Group, Paderborn University, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0215-1278","authenticated-orcid":false,"given":"Hamada M.","family":"Zahera","sequence":"additional","affiliation":[{"name":"Data Science Group, Paderborn University, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7112-3516","authenticated-orcid":false,"given":"Axel-Cyrille","family":"Ngonga-Ngomo","sequence":"additional","affiliation":[{"name":"Data Science Group, Paderborn University, Germany"}]}],"member":"7437","container-title":["Studies on the Semantic Web","Knowledge Graphs in the Age of Language Models and Neuro-Symbolic AI"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SSW240027","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,16]],"date-time":"2024-09-16T07:46:30Z","timestamp":1726472790000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SSW240027"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,11]]},"ISBN":["9781643685373"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/ssw240027","relation":{},"ISSN":["1868-1158","2215-0870"],"issn-type":[{"type":"print","value":"1868-1158"},{"type":"electronic","value":"2215-0870"}],"subject":[],"published":{"date-parts":[[2024,9,11]]}}}