Select, Label, Evaluate: Active Testing in NLP
Select, Label, Evaluate: Active Testing in NLP explores Reduce NLP model evaluation costs by up to 95% by intelligently selecting the most informative test samples for annotation.. Commercial viability score: 7/10 in NLP Evaluation.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
Estimated $9K - $13K over 6-10 weeks.
See exactly what it costs to build this -- with 3 comparable funded startups.
7-day free trial. Cancel anytime.
Discover the researchers behind this paper and find similar experts.
7-day free trial. Cancel anytime.
Related Papers
- Builds On ThisEvaluating Interactive 2D Visualization as a Sample Selection Strategy for Biomedical Time-Series Data AnnotationViability: 4.0
- Builds On ThisRich Insights from Cheap Signals: Efficient Evaluations via Tensor FactorizationViability: 2.0
- Builds On ThisAdaptEval: A Benchmark for Evaluating Large Language Models on Code Snippet AdaptationViability: 4.0
- Prior WorkActivation Matters: Test-time Activated Negative Labels for OOD Detection with Vision-Language ModelsViability: 7.0
- Prior WorkSpend Less, Reason Better: Budget-Aware Value Tree Search for LLM Agents
Related Resources
- What are the benefits of using synthetic data with the LLM as a Meta-Judge approach for NLP evaluation?(question)
- Here are 30-50 long-tail search questions for the topic of NLP Evaluation, based on the provided context:(question)
- What are the practical implications of Active Testing for reducing NLP evaluation expenses?(question)