## Introduction Large language models have become increasingly popular over the past few years, thanks to their ability to compose poems and answer complex questions. However, this has led to a problem: people are using LLM in situations where they shouldn't be. A new research study analyzes why this happens and how to teach models their errors. ## Methodology The research paper examines if the negative result is due to the absence of error patterns. The authors define criteria for identifying groups of instances that are prone to errors and analyze the model's meta-labels to find these patterns. ## Results The study finds that some methods can help reveal error patterns, but others don't. This could explain the negative result. ## Conclusion The analysis shows that teaching models their errors might be a solution to reduce errors. However, there are still many improvements to be made, such as creating more effective methods for discovering error patterns. ## Future prospects The research paper proposes a new metric for measuring the effectiveness of teaching with error patterns. The results of the study show that this approach can help reduce errors, but there is still much work to be done. ## User study A user study shows a positive effect from teaching with the proposed metric, unlike human-AI team accuracy.