Generalization Is Stability, Not Accuracy: Multi-Axis Evaluation of LLMs
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
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As large language models (LLMs) grow more capable, they are increasingly deployed in context-rich settings where task inputs are often accompanied by long, partially irrelevant context. In a controlled setting, we find that state-of-the-art models often appear robust to task-irrelevant context at the aggregate level: prepending it to benchmark questions causes little change in overall accuracy.
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