18 citations · 57 across the 15 of their papers we have counts for
15 papers
Prioritization of Risks from Artificial Intelligence: A Delphi Study of 272 International Experts
Alexander K. Saeri, Jess Graham, Michael Noetel +185
Artificial intelligence poses many risks, ranging from familiar present-day harms to unprecedented and potentially catastrophic ones. Effective risk management requires prioritizat…
Post-training makes large language models less human-like
Marcel Binz, Elif Akata, Abdullah Almaatouq +76
Large language models (LLMs) are increasingly used as surrogates for human participants, but it remains unclear which models best capture human behavior and why. To address this, w…
Tracing the ongoing emergence of human-like reasoning in Large Language Models
Paolo Morosi, Nikoleta Pantelidou, Fritz Günther +2
Humans effortlessly go beyond literal meanings: If you mow the lawn, I will give you fifty dollars, is typically understood as implying that the speaker will pay only if the lawn i…
Multilingual Large Language Models do not comprehend all natural languages to equal degrees
Natalia Moskvina, Raquel Montero, Masaya Yoshida +8
Large Language Models (LLMs) play a critical role in how humans access information. While their core use relies on comprehending written requests, our understanding of this ability…
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…
Language in Vivo vs. in Silico: Size Matters but Larger Language Models Still Do Not Comprehend Language on a Par with Humans Due to Impenetrable Semantic Reference
Vittoria Dentella, Fritz Guenther, Evelina Leivada
Understanding the limits of language is a prerequisite for Large Language Models (LLMs) to act as theories of natural language. LLM performance in some language tasks presents both…
The Quo Vadis of the Relationship between Language and Large Language Models
Evelina Leivada, Vittoria Dentella, Elliot Murphy
In the field of Artificial (General) Intelligence (AI), the several recent advancements in Natural language processing (NLP) activities relying on Large Language Models (LLMs) have…
A Sentence is Worth a Thousand Pictures: Can Large Language Models Understand Hum4n L4ngu4ge and the W0rld behind W0rds?
Evelina Leivada, Gary Marcus, Fritz Günther +1
Modern Artificial Intelligence applications show great potential for language-related tasks that rely on next-word prediction. The current generation of Large Language Models (LLMs…
Testing AI on language comprehension tasks reveals insensitivity to underlying meaning
Vittoria Dentella, Fritz Guenther, Elliot Murphy +2
Large Language Models (LLMs) are recruited in applications that span from clinical assistance and legal support to question answering and education. Their success in specialized ta…
DALL-E 2 Fails to Reliably Capture Common Syntactic Processes
Evelina Leivada, Elliot Murphy, Gary Marcus
Machine intelligence is increasingly being linked to claims about sentience, language processing, and an ability to comprehend and transform natural language into a range of stimul…