Marktechpost

JULY 22, 2024

However, acquiring such datasets presents significant challenges, including data scarcity, privacy concerns, and high data collection and annotation costs. Artificial (synthetic) data has emerged as a promising solution to these challenges, offering a way to generate data that mimics real-world patterns and characteristics.

AI Researcher 120

AI Researcher AI Research Data Scarcity Prompt Engineering 120

Unite.AI

JULY 5, 2024

However, the real power of LLM-driven synthetic data generation lies in more sophisticated techniques and applications. Advanced Techniques for Synthetic Data Generation 2.1 Prompt Engineering Prompt engineering is crucial for guiding LLMs to generate high-quality, relevant synthetic data.

LLM 257

LLM Prompt Engineering Prompt Engineer Data Scarcity 257

AWS Machine Learning Blog

MARCH 14, 2024

We provide an overview of key generative AI approaches, including prompt engineering, Retrieval Augmented Generation (RAG), and model customization. When applying these approaches, we discuss key considerations around potential hallucination, integration with enterprise data, output quality, and cost.

Generative AI 125

Generative AI Prompt Engineering Prompt Engineer AI 125

Unite.AI

MARCH 13, 2024

Regardless of the approach, the training process for DSLMs involves exposing the model to large volumes of domain-specific textual data, such as academic papers, legal documents, financial reports, or medical records. Issues such as data scarcity, bias, and noise can significantly impact model performance.

Natural Language Processing 203

Natural Language Processing Large Language Models Data Scarcity LLM 203