HellaSwag or HellaBad? 36% of this popular LLM benchmark contains errors

A recent analysis highlights the limitations of using traditional academic benchmarks to assess large language models (LLMs) for real-world applications. Despite performing well on benchmarks like Google's BIG-Bench, some models were found to be less effective in practical tasks such as copywriting and interactive assistance. This discrepancy has led to wasted efforts, as half of the launch decisions based on benchmark performance were inversely correlated with human evaluations on real-world tasks. The study also points out significant errors in popular datasets like HellaSwag, where 36% of the data contains inaccuracies, raising questions about the validity of these benchmarks. The discussion emphasizes the need for more relevant and accurate evaluation metrics that reflect the actual capabilities of LLMs in practical scenarios, stressing the importance of good data quality to ensure that AI systems can transition effectively from research settings to real-world applications.