Generative AI for Synthetic Data Generation: Methods, Challenges and the Future
Abstract
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.
Community
@librarian-bot recommend
This is an automated message from the Librarian Bot. I found the following papers similar to this paper.
The following papers were recommended by the Semantic Scholar API
- Best Practices and Lessons Learned on Synthetic Data for Language Models (2024)
- A Multi-Faceted Evaluation Framework for Assessing Synthetic Data Generated by Large Language Models (2024)
- Group-wise Prompting for Synthetic Tabular Data Generation using Large Language Models (2024)
- GPTA: Generative Prompt Tuning Assistant for Synergistic Downstream Neural Network Enhancement with LLMs (2024)
- Better Synthetic Data by Retrieving and Transforming Existing Datasets (2024)
Please give a thumbs up to this comment if you found it helpful!
If you want recommendations for any Paper on Hugging Face checkout this Space
You can directly ask Librarian Bot for paper recommendations by tagging it in a comment:
@librarian-bot
recommend
That's a promising paper! However, in the section III, authors mention the following part will present 2 scenarios, but I can only find one scenaria about medical. I am looking for the education scenarios.
Models citing this paper 0
No model linking this paper
Datasets citing this paper 0
No dataset linking this paper
Spaces citing this paper 0
No Space linking this paper