The article ‘Inadequacies of Large Language Model Benchmarks in the Era of Generative Artificial Intelligence’ critically assesses 23 state-of-the-art LLM benchmarks. The authors highlight significant limitations, such as biases, difficulties in measuring genuine reasoning, implementation inconsistencies, and overlooking of cultural and ideological norms. They emphasize the urgent need for standardized methodologies, regulatory certainties, and ethical guidelines in light of AI advancements. The study advocates for a shift from static benchmarks to dynamic behavioral profiling to accurately capture LLMs’ complex behaviors and potential risks. The authors underline the importance of collaborative efforts for the development of universally accepted benchmarks and the enhancement of AI systems’ integration into society.
Publication date: 15 Feb 2024
Project Page: arXiv:2402.09880v1
Paper: https://arxiv.org/pdf/2402.09880
