Summary of The “llm World Of Words” English Free Association Norms Generated by Large Language Models, By Katherine Abramski et al.

Main: Computation and Language (cs.CL)
Secondary: Artificial Intelligence (cs.AI)

The “LLM World of Words” English free association norms generated by large language models

by Katherine Abramski, Riccardo Improta, Giulio Rossetti, Massimo Stella

First submitted to arxiv on: 2 Dec 2024

GrooveSquid.com Paper Summaries

GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!

Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary The research paper presents a novel approach to investigating the biases encoded in Large Language Models (LLMs) using free association methods. The study creates a large-scale dataset of LLM-generated norms, modeled after human-generated norms, to facilitate this new direction of research. The authors prompt three LLMs with the same cues and generate comparable datasets. By analyzing both human and LLM-generated norms, the paper constructs cognitive network models of semantic memory, demonstrating the potential for investigating implicit biases in humans and LLMs.
Low	GrooveSquid.com (original content)	Low Difficulty Summary The study explores how Large Language Models can help us understand our own thoughts and biases. It creates a big dataset by asking many language models to associate certain words with others. This helps researchers see how human and computer-generated ideas are connected. The results show that computers can pick up on harmful gender stereotypes, just like humans do.