Toward Trustworthy Difficulty Assessments: Large Language Models as Judges in Programming and Synthetic Tasks

This article, sourced from ArXiv, focuses on the use of Large Language Models (LLMs) to assess the difficulty of programming and synthetic tasks. The core idea is to leverage LLMs as judges, potentially improving the reliability and validity of difficulty assessments. The research likely explores the capabilities of LLMs in understanding and evaluating task complexity, offering insights into how AI can be used to automate and enhance the process of evaluating the difficulty of various tasks.