Constrained SLM Scoring Cuts Processing Time 30% on 600-Task Benchmark

1 articles · Updated · KDnuggets · Aug 13

94.51 seconds versus 134.01 seconds: constraining a small language model to score only valid labels cut runtime by about 30% in a 600-ticket classification test.
One forward pass replaced multi-token text generation and regex-style parsing, making out-of-set answers structurally impossible while also producing a confidence score for each prediction.
The benchmark used Qwen2.5-0.5B-Instruct on an M2 MacBook Air; both methods returned 0 unparseable outputs, but the free-form approach still misclassified an email-change request as technical.
The article argues the method fits narrow automation tasks such as ticket routing, form extraction and document tagging, where fixed output sets and high call volumes favor small, local models over costlier LLM APIs.