Shot Natural Language Generation

Few-shot natural language generation (NLG) focuses on training models to generate high-quality text with minimal training data, addressing the limitations of data-hungry approaches. Current research emphasizes leveraging pre-trained large language models (LLMs) and diffusion models, often incorporating techniques like prompt engineering, parameter-efficient fine-tuning, and self-training to improve performance and generalization across diverse tasks, including image and tabular data generation. This area is significant because it enables the development of more efficient and adaptable NLG systems for applications where labeled data is scarce or expensive to obtain, such as personalized medicine and specialized information retrieval. The development of robust few-shot methods is crucial for broadening the accessibility and applicability of NLG technologies.