Ablation
Ablation is an experiment that removes or disables part of a system and measures how the result changes. Controlled comparisons help estimate what that component contributes.
Also known as: ablation study
A team evaluating a RAG assistant might compare retrieval plus reranking with retrieval alone. Keeping the generator, questions and scoring rules fixed helps reveal whether the reranker improves answers enough to justify its latency. This is an illustrative experiment, not a reported result.
Removing a component measures its contribution within that particular system. It does not show that the component is useless in every design: other parts may compensate for it, or depend on it. Changing the dataset and several components at once makes attribution harder.
State whether each variant is retrained or retuned. Disabling part of a fixed model and training a replacement without it answer different questions. Report the evaluation conditions and variation across runs so a small apparent gain does not become an unsupported architectural claim.
Sources
- Meyes et al.: Ablation Studies in Artificial Neural Networks — Sections 3.1–3.3 disable units, evaluate on shared test data and study recovery training separately.
Go deeper
- Meyes et al.: Ablation study code docs
Inspect the authors’ code and saved networks for reproducing component-removal experiments.