Search
 
loader loading subjects...
cover
Enhancing Answers Verbalization Using Large Language Models
Daniel Vollmers, Parth Sharma, Hamada M. Zahera, Axel-Cyrille Ngonga-Ngomo
345 - 352
10.3233/SSW240027
Research Article
Studies on the Semantic Web
Volume 60: Knowledge Graphs in the Age of Language Models and Neuro-Symbolic AI
Abstract

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.

Download
Creative Commons License