Generative AI For Synthetic Data Generation: Methods, Challenges And The Future

Guo Xu, Chen Yiqiang. Arxiv 2024

[Paper]    
Applications RAG Reinforcement Learning Training Techniques

The recent surge in research focused on generating synthetic data from large language models (LLMs), especially for scenarios with limited data availability, marks a notable shift in Generative Artificial Intelligence (AI). Their ability to perform comparably to real-world data positions this approach as a compelling solution to low-resource challenges. This paper delves into advanced technologies that leverage these gigantic LLMs for the generation of task-specific training data. We outline methodologies, evaluation techniques, and practical applications, discuss the current limitations, and suggest potential pathways for future research.

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