20 citations · 43 across the 6 of their papers we have counts for
6 papers
LLMs and people both learn to form conventions -- just not with each other
Cameron R. Jones, Agnese Lombardi, Kyle Mahowald +1
Humans align to one another in conversation -- adopting shared conventions that ease communication. We test whether LLMs form the same kinds of conventions in a multimodal communic…
Large Language Models Pass the Turing Test
Cameron R. Jones, Benjamin K. Bergen
We evaluated 4 systems (ELIZA, GPT-4o, LLaMa-3.1-405B, and GPT-4.5) in two randomised, controlled, and pre-registered Turing tests on independent populations. Participants had 5 mi…
Lies, Damned Lies, and Distributional Language Statistics: Persuasion and Deception with Large Language Models
Cameron R. Jones, Benjamin K. Bergen
Large Language Models (LLMs) can generate content that is as persuasive as human-written text and appear capable of selectively producing deceptive outputs. These capabilities rais…
GPT-4 is judged more human than humans in displaced and inverted Turing tests
Ishika Rathi, Sydney Taylor, Benjamin K. Bergen +1
Everyday AI detection requires differentiating between people and AI in informal, online conversations. In many cases, people will not interact directly with AI systems but instead…
Dissecting the Ullman Variations with a SCALPEL: Why do LLMs fail at Trivial Alterations to the False Belief Task?
Zhiqiang Pi, Annapurna Vadaparty, Benjamin K. Bergen +1
Recent empirical results have sparked a debate about whether or not Large Language Models (LLMs) are capable of Theory of Mind (ToM). While some have found LLMs to be successful on…
People cannot distinguish GPT-4 from a human in a Turing test
Cameron R. Jones, Benjamin K. Bergen
We evaluated 3 systems (ELIZA, GPT-3.5 and GPT-4) in a randomized, controlled, and preregistered Turing test. Human participants had a 5 minute conversation with either a human or…