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Generalization Is Stability, Not Accuracy: Multi-Axis Evaluation of LLMs

The paper introduces a new approach to evaluate generalization in large language models (LLMs) by focusing on stability across different input variations rather than relying on aggregate accuracy scores. It presents the Stability-Aware Generalization Objective (SAGO) framework, which measures how model behavior changes under various input formats and benchmarks, highlighting inconsistencies in model performance.

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PublishedOctober 1, 2026Nagham Omar, Mahmoud Jabarin, Maya Rozenshtein
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WHY IT MAY MATTER

This research provides a more nuanced understanding of model generalization by focusing on stability across different input variations, which can help in developing more reliable and consistent language models.

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