Regression Benchmarking

Benched.ai Editorial Team

Regression benchmarking repeatedly runs a fixed suite of tests across model versions to detect performance regressions in accuracy, latency, or safety.

Benchmark Components

Component	Description	Example
Dataset	Static prompts with ground-truth answers	MT-Bench subset
Metric	Quantitative score	BLEU, toxicity rate
Tolerance	Threshold for regression alert	Δ accuracy >1 %
Automation	CI pipeline executing tests	GitHub Actions

Workflow

Freeze benchmark dataset in version control.
After each model training run, execute benchmark.
Compare metrics to last blessed baseline.
Gate promotion if regression exceeds tolerance.

Design Trade-offs

Large suites catch more regressions but slow CI.
Human-graded metrics are accurate yet expensive; automatic proxies may miss nuances.
Overfitting to benchmark reduces generalization.

Current Trends (2025)

Crowd-sourced benchmark scoring pools reduce grading cost 60 %.
Differential diff metrics focus on new failure cases rather than aggregate drop¹.

Implementation Tips

Tag benchmark runs with git SHA for traceability.
Store full model answers to allow manual triage.
Visualize metric history to spot slow drifts.

MLPerf Inference 4.0, Regression Benchmarks for Generative Models, 2025. ↩