20 citations · 56 across the 10 of their papers we have counts for
6 papers · 1 filter
You Shouldn't Have Asked: A Pragmatics-Inspired Taxonomy for Evaluating LLM Refusals
Ruoxuan Li, Pinqiao Wang, Sheng Li +1
Refusals are often treated as face-threatening acts in pragmatics because they can challenge the requester's socially claimed self-image. Large language models (LLMs) are increasin…
ConvApparel: A Benchmark Dataset and Validation Framework for User Simulators in Conversational Recommenders
Ofer Meshi, Krisztian Balog, Sally Goldman +5
The promise of LLM-based user simulators to improve conversational AI is hindered by a critical "realism gap," leading to systems that are optimized for simulated interactions, but…
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…
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…