New SAGE Benchmark Reveals Nuanced Semantic Understanding Trade-offs
A new benchmark, SAGE, has been introduced to rigorously evaluate semantic understanding in language models and similarity metrics. It assesses performance across five key categories, providing a more realistic and challenging evaluation than previous methods.
SAGE evaluates models under adversarial conditions using noisy transformations and nuanced human judgment tasks across over 30 datasets. It has revealed nuanced performance trade-offs, with embedding models generally outperforming classical metrics in tasks requiring deep semantic understanding, but classical metrics excelling in information sensitivity and transformation robustness.
Notably, Jaccard Similarity achieved a score of 0.905 in information sensitivity, surpassing the top embedding score of 0.794. Among embedding models, OpenAI's text-embedding-3-large achieved the highest overall SAGE score of 0.524. However, even the most robust approaches retain only around 67% effectiveness in noisy environments, highlighting the need for defensive architectures and safeguards in critical applications.
SAGE, developed by researchers at the University of California, is a significant advancement in evaluating semantic understanding. The study, titled 'SAGE: A Realistic Benchmark for Semantic Understanding', is published on arXiv and can be accessed at https://arxiv.org/abs/2509.21310. For academic inquiries, researchers can typically be contacted through their university departments or research institution websites.
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