[Submitted on 21 Jul 2026]
Title:Do small language models know what they don't know?
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Abstract:We explore whether entropy-based confidence signals can be leveraged to improve the accuracy of Small Language Models (SLMs) with fewer than 3 billion parameters, running entirely on consumer hardware. We evaluate seven distinct approaches, including token-level entropy early stopping, semantic entropy estimation, and uncertainty-aware routing to larger expert models, across 7 model pairs and 5 standard NLU benchmarks. Our key finding is that token-level entropy is effectively blind in SLMs: in 91% of dataset-model combinations, mean token entropy is near zero regardless of answer correctness, rendering token-based confidence signals unusable at this scale. We demonstrate that semantic entropy, computed by generating multiple samples, clustering answers by meaning, and measuring distributional uncertainty, recovers a viable confidence signal. Using semantic entropy to selectively route uncertain queries to a larger expert model yields accuracy improvements of up to +50 percentage points. Notably, cross-family routing (e.g., SmolLM 360M to Phi-3.5-mini) averages +22.0% improvement compared to +6.8% for same-family routing, revealing that expert model quality matters more than architectural compatibility. Our results suggest that the value proposition for entropy-based methods in SLMs is not computational savings but intelligent compute allocation: spending more tokens where they matter most.
Comments: 9 pages, 8 figures
Subjects:
Computation and Language (cs.CL)
Cite as: arXiv:2609.20824 [cs.CL]
(or arXiv:2609.20824v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.20824
arXiv-issued DOI via DataCite
Submission history
From: Prashant Mudgal [view email] [v1] Tue, 21 Jul 2026 18:23:46 UTC (1,451 KB)
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