Synthetic Data Generation
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Benchmarks
UNSW-NB15
Most implemented
Wasserstein GAN
Improved Training of Wasserstein GANs
Generating Multidimensional Clusters With Support Lines
Using GANs for Sharing Networked Time Series Data: Challenges, Initial Promise, and Open Questions
Synthetic QA Corpora Generation with Roundtrip Consistency
Papers
Calibration as a First-Class Criterion in LLM Evaluation
Calibration of language models -- the alignment between expressed or implicit confidence and empirical correctness -- is a well-studied subfield within NLP. Methods to measure it already exist. The problem is adoption: o…
Synthetic Data GenerationActive LearningEDGEGEN: Improving Tool-Calling Agents Beyond Happy Paths with Synthetic Edge Case Generation
Tool-calling LLM agents are increasingly deployed in enterprise applications. However, effective evaluation and optimization require high-quality, diverse task datasets that are often difficult to obtain due to privacy a…
Synthetic Data GenerationA Framework for Generating Valid Context-Specific Benchmarks through Expert Guidance
This paper presents an end-to-end approach for generating context-specific large language model (LLM) benchmark datasets by combining expert input with synthetic data generation. Existing benchmark construction methods o…
Synthetic Data GenerationSpot-the-shift: Evaluating Grounded Image Difference Captioning of Long-term Changes
Long-term change understanding from images of the same place revisited over time is a challenging task with applications in map maintenance and urban infrastructure monitoring. Prior work addresses it either through pixe…
Synthetic Data GenerationSynThermFace: Amplifying Limited Paired Data for Visible-Thermal Face Recognition via Synthetic Data Generation
Face recognition (FR) is a widely used modality for biometric authentication, but conventional models rely on visible-spectrum imagery and degrade when high-quality RGB images cannot be captured. Cross-spectral face reco…
Synthetic Data GenerationFace RecognitionFrom Documents to Reasoning: A Validated Synthetic Data Pipeline and Semantic-Aware Fine-Tuning for Financial Numerical Reasoning
Financial question answering (QA) has emerged as a key benchmark for evaluating the performance of Large Language Models (LLMs) on domain-specific tasks involving complex data formats such as tables, charts, and rich tex…
Synthetic Data GenerationSemantic SimilarityQuestion AnsweringAnswer Generation