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AI agent benchmarks are misleading, study warns
A study by Princeton University shows that benchmarks made for AI agents don't account for costs and are prone to overfitting.
However, a recent analysis by researchers at Princeton University has revealed several shortcomings in current agent benchmarks and evaluation practices that hinder their usefulness in real-world applications. The Princeton researchers propose visualizing evaluation results as a Pareto curve of accuracy and inference cost and using techniques that jointly optimize the agent for these two metrics. To address this problem, the researchers suggest that benchmark developers should create and keep holdout test sets that are composed of examples that can’t be memorized during training and can only be solved through a proper understanding of the target task.
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