DATA & COMPUTATIONAL WORKFLOWS
NLP Experiments Lab
Test instruction effects, compare outputs, and run a defined linguistic task while keeping final judgment with the researcher.
Outputs are AI-supported analytical suggestions. The researcher remains responsible for verifying and approving results for research use.
Execution type: AI-supported inference
How are NLP tasks executed here?
NLP Task Sandbox executes the task using an AI model through the API under researcher-defined instructions, labels, and output structure. The result is returned in a structured format, validated against the defined constraints, and remains subject to researcher acceptance, modification, or rejection.
This differs from running an independent deterministic NLP algorithm such as Naive Bayes, TF-IDF, or a rule-based morphological analyzer. When LinguaLab uses deterministic computation, it is labeled explicitly.
- 1Researcher defines task
- 2UI builds a structured request
- 3API executes an AI model
- 4Structured JSON response
- 5Programmatic validation
- 6Researcher Accept / Modify / Reject
COMPUTATIONAL WORKBENCH
How this research workflow processes your data
- 01Fixed InputText and task
- 02Experiment SetupInstructions, labels, or structure
- 03Experiment RunExplicit action
- 04Structured OutputsPreserved A and B outputs
- 05ComparisonDecision, evidence, explanation, consistency
- 06Researcher EvaluationNo automatic winner
- 07Error InspectionRecord the reason
- 08ImprovementAdjust setup, then re-run
The model produces structured suggestions for researcher review; it does not create measured results or human reference labels.