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Do small language models know what they don't know?
Author1, Author2, Author3, Author4, Author5
entropy-based methodssmall language modelsconfidence signalsexpert model routing
2609.20824
Builder Relevance
1h ago80%
Abstract
This paper explores the use of entropy-based confidence signals to enhance the accuracy of Small Language Models (SLMs) with fewer than 3 billion parameters.
Reality Card
Core Claim
Semantic entropy can effectively improve accuracy in SLMs by up to +50 percentage points when routing uncertain queries to larger expert models.
Method / Result
Using semantic entropy for routing yields an average accuracy improvement of +22.0% for cross-family routing.
Limitations
Token-level entropy is ineffective in SLMs, with mean token entropy near zero in 91% of cases, limiting the applicability of token-based confidence signals.
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