LLMs Evolving: Tackling Fictionalization of Facts

This article explores the fascinating ways in which **Large Language Models (LLMs)** can sometimes misinterpret and categorize new information, treating it as fiction. It highlights the technical and practical considerations of this phenomenon, offering insights into how to work with these evolving models effectively.

• •The article categorizes LLM errors into four levels, from knowledge gaps to the 'SF setting' type.
• •The study highlights 'knowledge conflict' and 'over-refusal' as factors contributing to fact misinterpretation.
• •The article focuses on methods to mitigate LLMs categorizing correct current information as fiction.