Zero-Shot Confidence Estimation for Small LLMs: When Supervised Baselines Aren't Worth Training
For practitioners deploying cost-effective LLM routing, this work provides a training-free confidence estimation method that outperforms supervised approaches, reducing the need for labeled data.
The paper shows that zero-shot confidence signals, particularly average token log-probability, match or exceed supervised baselines for small LLMs' self-assessment, achieving AUROC 0.650-0.714 in-distribution and 0.717-0.833 out-of-distribution, while supervised baselines require training data and fail to outperform zero-shot methods.
How reliably can a small language model estimate its own correctness? The answer determines whether local-to-cloud routing-escalating queries a cheap local model cannot handle-can work without supervised training data. As inference costs dominate large language model (LLM) deployment budgets, routing most queries to a cheap local model while reserving expensive cloud calls for hard cases is an increasingly common cost-control strategy. We compare zero-shot confidence signals against RouteLLM-style supervised baselines across three 7-8B model families and two datasets (1,000 and 500 queries per model, respectively). Average token log-probability, which requires no training data, matches or exceeds supervised baselines in-distribution (Area Under the Receiver Operating Characteristic curve (AUROC) 0.650-0.714 vs. 0.644-0.676) and substantially outperforms them out-of-distribution (0.717-0.833 vs. 0.512-0.564), because it measures a property of the model's generation rather than the query distribution. This paper further proposes retrieval-conditional self-assessment, a pre-generation signal that selectively injects retrieved knowledge when similarity is high, improving over bare self-assessment by up to +0.069 AUROC at 3-10x lower latency than log-probability. A supervised baseline trained on 1,000 labeled examples never exceeds the zero-shot signal. We release all code, data, and experiment logs.