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Main Authors: Moisio, Anssi, Creutz, Mathias, Kurimo, Mikko
Format: Preprint
Published: 2024
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Online Access:https://arxiv.org/abs/2407.08269
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author Moisio, Anssi
Creutz, Mathias
Kurimo, Mikko
author_facet Moisio, Anssi
Creutz, Mathias
Kurimo, Mikko
contents Rule-based language processing systems have been overshadowed by neural systems in terms of utility, but it remains unclear whether neural NLP systems, in practice, learn the grammar rules that humans use. This work aims to shed light on the issue by evaluating state-of-the-art LLMs in a task of morphological analysis of complex Finnish noun forms. We generate the forms using an FST tool, and they are unlikely to have occurred in the training sets of the LLMs, therefore requiring morphological generalisation capacity. We find that GPT-4-turbo has some difficulties in the task while GPT-3.5-turbo struggles and smaller models Llama2-70B and Poro-34B fail nearly completely.
format Preprint
id arxiv_https___arxiv_org_abs_2407_08269
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle LLMs' morphological analyses of complex FST-generated Finnish words
Moisio, Anssi
Creutz, Mathias
Kurimo, Mikko
Computation and Language
Rule-based language processing systems have been overshadowed by neural systems in terms of utility, but it remains unclear whether neural NLP systems, in practice, learn the grammar rules that humans use. This work aims to shed light on the issue by evaluating state-of-the-art LLMs in a task of morphological analysis of complex Finnish noun forms. We generate the forms using an FST tool, and they are unlikely to have occurred in the training sets of the LLMs, therefore requiring morphological generalisation capacity. We find that GPT-4-turbo has some difficulties in the task while GPT-3.5-turbo struggles and smaller models Llama2-70B and Poro-34B fail nearly completely.
title LLMs' morphological analyses of complex FST-generated Finnish words
topic Computation and Language
url https://arxiv.org/abs/2407.08269