18 citations · 57 across the 15 of their papers we have counts for
6 papers · 1 filter
Quantification and object perception in Multimodal Large Language Models and human linguistic cognition
Raquel Montero, Natalia Moskvina, Paolo Morosi +3
Quantification has been proven to be a particularly difficult linguistic phenomenon for (Multimodal) Large Language Models (MLLMs). However, given that quantification interfaces wi…
Community size rather than grammatical complexity better predicts Large Language Model accuracy in a novel Wug Test
Nikoleta Pantelidou, Evelina Leivada, Raquel Montero +1
The linguistic abilities of Large Language Models are a matter of ongoing debate. This study contributes to this discussion by investigating model performance in a morphological ge…
Large Language Model probabilities cannot reliably distinguish between possible and impossible language
Evelina Leivada, Raquel Montero, Paolo Morosi +5
A controversial test for Large Language Models concerns the ability to discern possible from impossible language. While some evidence attests to the models sensitivity to what cros…
ChatGPT-generated texts show authorship traits that identify them as non-human
Vittoria Dentella, Weihang Huang, Silvia Angela Mansi +2
Large Language Models can emulate different writing styles, ranging from composing poetry that appears indistinguishable from that of famous poets to using slang that can convince…
When Large Language Models are More PersuasiveThan Incentivized Humans, and Why
Jiacheng Liu, Francesco Salvi, Philipp Schoenegger +37
Large Language Models (LLMs) have been shown to be highly persuasive, but when and why they outperform humans is still an open question. We compare the persuasiveness of two LLMs (…
Fundamental Principles of Linguistic Structure are Not Represented by o3
Elliot Murphy, Evelina Leivada, Vittoria Dentella +2
A core component of a successful artificial general intelligence would be the rapid creation and manipulation of grounded compositional abstractions and the demonstration of expert…