You must pick the best model from an experiment. What should guide you?
Why B is the answer
Metrics generated in the experiments
Metrics from your own experiments show how each model performs on your task, which public opinion cannot.
In detail
- Metrics from experiments give evidence for choosing a model.
- Those metrics reflect your own task.
- Compare cost and latency as well as quality.
Use case
An illustrative scenario, not a real incident.
A team tests three models on its own data in an experiment. One is slightly less accurate but far cheaper, and the metrics make that trade-off plain.
See it in different ways
sequenceDiagram participant E as Experiment participant M as Candidates participant D as Decision E->>M: Same data and tasks M-->>E: Metrics E->>D: Compare cost, latency and quality D-->>E: Choose, and record why
flowchart LR S0["Run the experiment"] --> S1["Compare metrics"] --> S2["Weigh cost and latency"] --> S3["Choose"]
Key point: Your experiment metrics decide.
Why the other options are wrong
- A. The most recent blog post about it
- A blog post is not evidence from your data.
- C. The date that each model was released to the public
- A recent date does not prove a better fit.
- D. Its rank on a public leaderboard
- A public rank is not measured on your task.