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20242026
most citedWhen Large Language Models are More PersuasiveThan Incentivized Humans, and Why

7 citations · 13 across the 7 of their papers we have counts for

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7 papers · 1 filter

cs.CL2026

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…

cs.CL2026

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…

cs.CL2026

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…

cs.CL2025★ 1 cited

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…

cs.CL2025★ 7 cited

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 (…

cs.CL2025★ 3 cited

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…

cs.CL2024★ 2 cited

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…