Inadequacies of Large Language Model Benchmarks in the Era of Generative Artificial Intelligence
This addresses the problem of inadequate evaluation methods for LLMs, which is crucial for researchers and developers in AI, though it is incremental in proposing improvements rather than a completely new solution.
The study critically assessed 23 state-of-the-art LLM benchmarks using a novel unified framework, uncovering significant limitations such as biases and difficulties in measuring reasoning, and emphasized the need for a paradigm shift toward dynamic behavioral profiling and standardized methodologies.
The rapid rise in popularity of Large Language Models (LLMs) with emerging capabilities has spurred public curiosity to evaluate and compare different LLMs, leading many researchers to propose their own LLM benchmarks. Noticing preliminary inadequacies in those benchmarks, we embarked on a study to critically assess 23 state-of-the-art LLM benchmarks, using our novel unified evaluation framework through the lenses of people, process, and technology, under the pillars of benchmark functionality and integrity. Our research uncovered significant limitations, including biases, difficulties in measuring genuine reasoning, adaptability, implementation inconsistencies, prompt engineering complexity, evaluator diversity, and the overlooking of cultural and ideological norms in one comprehensive assessment. Our discussions emphasized the urgent need for standardized methodologies, regulatory certainties, and ethical guidelines in light of Artificial Intelligence (AI) advancements, including advocating for an evolution from static benchmarks to dynamic behavioral profiling to accurately capture LLMs' complex behaviors and potential risks. Our study highlighted the necessity for a paradigm shift in LLM evaluation methodologies, underlining the importance of collaborative efforts for the development of universally accepted benchmarks and the enhancement of AI systems' integration into society.