SLM Evaluation — Measuring Small Model Quality

Why it matters

Wrong evaluation leads to wrong model choice. A model with better benchmark scores can be worse on your actual task. Building good evaluation is what separates production-ready teams.

The architecture

General benchmarks: MMLU (broad knowledge), GSM8K (grade-school math), HumanEval (code), TruthfulQA (accuracy), Big-Bench (broad).

Task benchmarks: build your own eval set matching production distribution.

How it works end to end

General benchmarks compare models at a broad level. Different SLMs excel at different tasks; benchmarks reveal this.

Task-specific eval: sample production data, define success criteria, evaluate candidate models. Weight most heavily.

LLM-as-judge: use strong model to score responses. Fast and scales; watch for biases (verbosity, position).