Research · Datasets
A dataset is an evidence object, not a folder of text.
Useful training and evaluation data needs traceable origin, documented rights, explicit transformations, controlled splits, and a reproducible identity. Volume alone does not establish quality.
Every approved release should answer: where did it come from, what changed, what is it for, and how do we reproduce it?
Different Jobs
Do not treat every collection as training data.
Training dataset
Examples selected and formatted to change model behavior for a defined task. It needs licensing, privacy, quality, balance, and leakage review.
Retrieval corpus
Material indexed to supply attributable context at run time. It can be updated independently of model weights and should preserve source lineage.
Evaluation set
Frozen, isolated examples used to compare a candidate with a baseline. It must not leak into training or teacher-generated data.
Benchmark or challenge set
A repeatable stress test for capabilities, refusals, robustness, and regressions. Passing one benchmark is not a general claim of safety or quality.
Lifecycle
From source to immutable release.
Provenance & rights
Record source, license or permission, collection date, intended use, restrictions, and any privacy or sensitivity decision.
Normalize & label
Standardize encoding and schema, preserve important literals, label synthetic records, and document every transformation.
Control quality & leakage
De-duplicate, test exact and near-duplicate overlap, split related example families together, and isolate held-out evaluation data.
Freeze & identify
Create an immutable snapshot with stable record identifiers, counts, schema version, checksums, validation results, and a manifest.
Quality Controls
Quality is a chain of decisions.
- Document licenses, permissions, privacy, and retention.
- Keep transformation history and stable record identifiers.
- Label synthetic, augmented, negative, and abstention examples.
- Validate tokenizer behavior, chat templates, targets, and loss masks.
- Test for exact, fuzzy, and semantic duplication across splits.
- Support correction, deletion, and reproducible rebuilds.
Current Evidence
What is implemented versus what is merely collected.
The reviewed materials support a reproducible dataset build and quality-control workflow, including canonical schemas, validation, manifests, and a corrected rebuild.
They also include structured research collections. A structured corpus is not automatically approved training data, and its existence is not proof that a model was trained on it.
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