11 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…
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…
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…
When Large Language Models are More PersuasiveThan Incentivized Humans, and Why
Philipp Schoenegger, Francesco Salvi, Jiacheng Liu +39
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 (…