Toward Trustworthy Difficulty Assessments: Large Language Models as Judges in Programming and Synthetic Tasks
NegativeArtificial Intelligence
- Large Language Models (LLMs) like GPT-4o have been evaluated for their effectiveness in assessing the difficulty of programming tasks, specifically through a comparison with a Light-GBM ensemble model. The study revealed that Light-GBM achieved 86% accuracy in classifying LeetCode problems, while GPT-4o only reached 37.75%, indicating significant limitations in LLMs for structured assessments.
- This development highlights the challenges faced by LLMs in accurately interpreting numeric and contextual cues essential for difficulty assessment in programming tasks. The findings raise concerns about the reliability of LLMs as evaluators in educational and competitive contexts.
- The ongoing discourse around the capabilities and limitations of LLMs is underscored by similar studies questioning their stability and reliability across various applications. As LLMs are increasingly integrated into diverse fields, understanding their shortcomings is crucial for developing more effective AI systems and ensuring responsible deployment.
— via World Pulse Now AI Editorial System
