Autori
Zamparelli, RobertoChesi, CristianoVespignani, FrancescoTitolo
Modelli generativi e sintassi generativaPeriodico
Sistemi intelligentiAnno:
2023 - Fascicolo:
2 - Pagina iniziale:
329 - Pagina finale:
350In this short paper we present the results of four experiments assessing various degree of morphosyntactic and semantic linguistic competence in three very large language models (LLMs), namely davinci (GPT-3/ChatGPT), davinci-002 and davinci-003 (GPT-3.5 with different training options). We focused on (i) acceptability, (ii) complexity and (iii) coherence judgments on 7-point Likert scales and on (iv) syntactic development by means of a forced choice task. The datasets used are taken from available test-sets presented in shared tasks by the NLP community or from linguistic tests. The results suggest that, despite a rather good performance overall, these LLMs cannot be considered competence models since they do not qualify neither as descriptively nor explanatorily adequate
SICI: 1120-9550(2023)2<329:MGESG>2.0.ZU;2-7
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