A Calibrated Instrument for Measuring How Inference Optimizations Affect Output Quality
Large language model optimization is an active research area, spanning quantization of model weights, early-exit methods for skipping layers, and speculative decoding. Each track uses its own quality measures, typically an idiosyncratic benchmark score. Few approach the measurement precision required by other scientific disciplines. We propose a rigorous methodology for measuring output quality, suitable for cross-system and cross-technique comparison. We score outputs with an LLM as a judge, but calibrate the judge formally: we compare its scores on two ordinary runs of a model given the same prompts, verifying that it shows no systematic preference between statistically equivalent outpu...
| Authors | Jerry Kaplan |
|---|---|
| Primary category | cs.CL |
| Published | 16 September 2026 |
| Last updated at source | 16 September 2026 |
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