Data Reliability Crisis in LLM Evaluation: A Case Study

This article highlights a critical issue in evaluating Large Language Models: the unreliability of the data used for assessment. It underscores the importance of carefully curating and validating datasets to ensure accurate performance metrics.

• •Unreliable data leads to inaccurate LLM performance evaluations.
• •Prompt selection methodology significantly impacts results.
• •Careful data curation and validation are essential for reliable AI research.