#temperature-scaling News & Analysis

2 articles tagged with #temperature-scaling. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

2 articles

AINeutralarXiv – CS AI · Jun 26/10

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Rethinking the Role of Temperature in Large Language Model Distillation

Researchers demonstrate that temperature scaling fundamentally alters the performance comparison between forward KL and reverse KL divergence in LLM distillation, revealing that forward KL substantially outperforms reverse KL at higher temperatures by better leveraging non-dominant token signals. This finding challenges the prevailing preference for reverse KL and suggests that temperature optimization enables simple KL-based methods to match state-of-the-art distillation approaches.

AINeutralarXiv – CS AI · May 286/10

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The Well-Tempered Classifier: Some Elementary Properties of Temperature Scaling

Researchers provide the first rigorous theoretical analysis of temperature scaling, a widely-used technique for controlling uncertainty in machine learning models. The study reveals that while temperature scaling reliably increases entropy in classifiers, it does not necessarily increase diversity in large language models as commonly claimed, and establishes temperature scaling as the unique linear calibration method that preserves hard predictions.