generative artificial intelligence; linguistic linked open data; prompting evaluation
Abstract :
[en] This article addresses the question of evaluating generative AI prompts designed for specific tasks such as linguistic linked open data modelling and refining of word embedding results. The prompts were created to assist the pre-modelling phase in the construction of LLODIA, a linguistic linked open data model for diachronic analysis. We present a self-evaluation framework based on the method known in literature as LLM-Eval. The discussion includes prompts related to the RDF-XML conception of the model, and neighbour list refinement, dictionary alignment and contextualisation for the term revolution in French, Hebrew and Lithuanian, as a proof of concept.
Disciplines :
Computer science
Author, co-author :
ARMASELU, Florentina ; University of Luxembourg > Luxembourg Centre for Contemporary and Digital History (C2DH) > Digital History and Historiography
Liebeskind, Chaya; Jerusalem College of Technology, Israel
Valunaite Oleskeviciene, Giedre; Mykolas Romeris University, Lithuania
External co-authors :
yes
Language :
English
Title :
Self-Evaluation of Generative AI Prompts for Linguistic Linked Open Data Modelling in Diachronic Analysis
Publication date :
31 May 2024
Event name :
Workshop on Deep Learning and Linked Data (DLnLD) @ LREC-COLING 2024
Event organizer :
ELRA Language Resources Association (ELRA) and the International Committee on Computational Linguistics (ICCL)
