Summary of Redefining “hallucination” in Llms: Towards a Psychology-informed Framework For Mitigating Misinformation, by Elijah Berberette et al.

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

Redefining “Hallucination” in LLMs: Towards a psychology-informed framework for mitigating misinformation

by Elijah Berberette, Jack Hutchins, Amir Sadovnik

First submitted to arxiv on: 1 Feb 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 abstract discusses the limitations of large language models (LLMs) in accurately understanding and generating human-like responses. While these models are widely used by over a billion users, they often exhibit “hallucinations” – producing misinformation with confidence. The authors propose a psychological taxonomy based on cognitive biases to better understand this phenomenon and develop strategies to mitigate its effects.
Low	GrooveSquid.com (original content)	Low Difficulty Summary Large language models have become incredibly popular, but they can also produce misinformation in a confident manner. This is known as “hallucination.” Researchers are trying to understand why this happens and how to stop it from happening again. They want to make sure that these models can be trusted to give accurate answers.