This paper explores the employment of LLMs, specifically of Mistral-Nemo, in the semi-automatic population of the Ancient Greek WordNet synsets. Several approaches are investigated: zero-shot, few-shots, and fine-tuning. The results are compared against an English baseline. Zero-shot approach yields the highest accuracy, while fine-tuning leads to the highest number of potential synonyms. Our analysis also reveals that polysemy and PoS play a role in the model’s performance, as the highest scores are registered for polysemous words and for verbs and nouns. The results are encouraging for the application of such approaches in a human-in-the-loop scenario, since human validation still proves crucial in ensuring the accuracy of the results.
Towards the Semi-Automated Population of the Ancient Greek WordNet
Beatrice Marchesi;Annachiara Clementelli;Silvia Zampetta;Erica Biagetti;Luca Brigada Villa;Virginia Mastellari;Riccardo Ginevra;Claudia Roberta Combei;Chiara Zanchi
2025-01-01
Abstract
This paper explores the employment of LLMs, specifically of Mistral-Nemo, in the semi-automatic population of the Ancient Greek WordNet synsets. Several approaches are investigated: zero-shot, few-shots, and fine-tuning. The results are compared against an English baseline. Zero-shot approach yields the highest accuracy, while fine-tuning leads to the highest number of potential synonyms. Our analysis also reveals that polysemy and PoS play a role in the model’s performance, as the highest scores are registered for polysemous words and for verbs and nouns. The results are encouraging for the application of such approaches in a human-in-the-loop scenario, since human validation still proves crucial in ensuring the accuracy of the results.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
