SOTAVerified

Dataset Generation

The task involves enhancing the training of target application (e.g. autonomous driving systems) by generating datasets of diverse and critical elements (e.g. traffic scenarios). Traditional methods rely on expensive and limited datasets, which often fail to capture rare but essential situations that can pose risks during testing.

Papers

Showing 131140 of 308 papers

TitleStatusHype
SciFaultyQA: Benchmarking LLMs on Faulty Science Question Detection with a GAN-Inspired Approach to Synthetic Dataset GenerationCode0
Unbiased General Annotated Dataset Generation0
JAPAGEN: Efficient Few/Zero-shot Learning via Japanese Training Dataset Generation with LLMCode0
VariFace: Fair and Diverse Synthetic Dataset Generation for Face Recognition0
SynFinTabs: A Dataset of Synthetic Financial Tables for Information and Table ExtractionCode0
An Evolutionary Large Language Model for Hallucination Mitigation0
Know Your RAG: Dataset Taxonomy and Generation Strategies for Evaluating RAG Systems0
HyperFace: Generating Synthetic Face Recognition Datasets by Exploring Face Embedding Hypersphere0
Drone Detection using Deep Neural Networks Trained on Pure Synthetic DataCode0
CorrSynth -- A Correlated Sampling Method for Diverse Dataset Generation from LLMs0
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