Evaluation Reference Dataset
(Redirected from gold standard dataset)
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An Evaluation Reference Dataset is an evaluation dataset that provides curated reference standards serving as quality benchmarks for system evaluation across NLP tasks.
- AKA: Reference Standard Dataset, Benchmark Reference Dataset, Gold Standard Dataset, Evaluation Benchmark Collection.
- Context:
- It can typically undergo Quality Validation Processes ensuring reference reliability.
- It can typically support Reproducible Evaluation through standardized references.
- It can often include Multiple Reference Versions capturing acceptable variation.
- It can often enable Automatic Metrics requiring reference comparison.
- It can provide Annotation Metadata documenting creation process.
- It can maintain Version Control for longitudinal comparison.
- It can facilitate Cross-System Benchmarking with consistent standards.
- It can incorporate Domain Balance across application areas.
- It can range from being a Small Reference Dataset to being a Large Reference Dataset, depending on its dataset size.
- It can range from being a Single-Reference Dataset to being a Multi-Reference Dataset, depending on its reference count per instance.
- It can range from being a Human-Created Reference Dataset to being a Hybrid Reference Dataset, depending on its creation method.
- It can range from being a Static Reference Dataset to being a Evolving Reference Dataset, depending on its update pattern.
- ...
- Examples:
- Task-Specific Reference Datasets, such as:
- Benchmark Collections, such as:
- Domain Reference Datasets, such as:
- ...
- Counter-Examples:
- Training Dataset, which serves model development.
- Synthetic Dataset, which lacks human curation.
- Raw Corpus, which lacks reference annotation.
- See: Evaluation Dataset, NLG Gold Reference Dataset, Benchmark Dataset, Reference Standard, Annotation Process, Dataset Curation, Evaluation Framework.