3 citations · 4 across the 6 of their papers we have counts for
6 papers
The role of System 1 and System 2 semantic memory structure in human and LLM biases
Katherine Abramski, Giulio Rossetti, Massimo Stella
Implicit biases in both humans and large language models (LLMs) pose significant societal risks. Dual process theories propose that biases arise primarily from associative System 1…
A word association network methodology for evaluating implicit biases in LLMs compared to humans
Katherine Abramski, Giulio Rossetti, Massimo Stella
As Large language models (LLMs) become increasingly integrated into our lives, their inherent social biases remain a pressing concern. Detecting and evaluating these biases can be…
The "LLM World of Words" English free association norms generated by large language models
Katherine Abramski, Riccardo Improta, Giulio Rossetti +1
Free associations have been extensively used in cognitive psychology and linguistics for studying how conceptual knowledge is organized. Recently, the potential of applying a simil…
Y Social: an LLM-powered Social Media Digital Twin
Giulio Rossetti, Massimo Stella, Rémy Cazabet +7
In this paper we introduce Y, a new-generation digital twin designed to replicate an online social media platform. Digital twins are virtual replicas of physical systems that allow…
From Perils to Possibilities: Understanding how Human (and AI) Biases affect Online Fora
Virginia Morini, Valentina Pansanella, Katherine Abramski +4
Social media platforms are online fora where users engage in discussions, share content, and build connections. This review explores the dynamics of social interactions, user-gener…
Cognitive network science reveals bias in GPT-3, ChatGPT, and GPT-4 mirroring math anxiety in high-school students
Katherine Abramski, Salvatore Citraro, Luigi Lombardi +2
Large language models are becoming increasingly integrated into our lives. Hence, it is important to understand the biases present in their outputs in order to avoid perpetuating h…