Calibrating Small Language Models for Claim Check-Worthiness Detection
Assessing claim check-worthiness is an essential first step in automated fact-checking pipelines. This work is motivated by a real deployment challenge at an early-stage startup: running large language models (LLMs) over every incoming claim is cost- and latency-prohibitive, yet smaller models sacrifice accuracy. We propose NN-PPI, a pointwise extension of Prediction-Powered Inference (PPI) that calibrates model predictions at inference time as a lightweight post-hoc layer, without re-training the underlying model. NN-PPI achieves weighted F1 gains ranging from 12% to 33.80% depending on the size and performance of the baseline model, bringing SLMs on par with larger LLMs. Beyond few-shot...
| Authors | Pratuat Amatya, V Venktesh, Vinay Setty |
|---|---|
| Primary category | cs.CL |
| Published | 31 August 2026 |
| Last updated at source | 1 September 2026 |
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