Unite.AI

JULY 5, 2024

This capability is changing how we approach AI development, particularly in scenarios where real-world data is scarce, expensive, or privacy-sensitive. In this comprehensive guide, we'll explore LLM-driven synthetic data generation, diving deep into its methods, applications, and best practices.

LLM 259

LLM Prompt Engineering Prompt Engineer Data Scarcity 259

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 Research 122

AI Research AI Researcher Data Scarcity Prompt Engineer 122

Marktechpost

MARCH 3, 2025

Data Scarcity: Pre-training on small datasets (e.g., In conclusion, NeoBERT represents a paradigm shift for encoder models, bridging the gap between stagnant architectures and modern LLM advancements. All credit for this research goes to the researchers of this project. faster than ModernBERT, despite larger size.

BERT 75

BERT Data Scarcity Natural Language Processing Large Language Models 75

Marktechpost

SEPTEMBER 2, 2023

Researchers from Chinese Academy of Sciences, University of Chinese Academy of Sciences, Objecteye Inc., and Wuhan AI Research present AnomalyGPT, a unique IAD methodology based on LVLM, as shown in Figure 1, as neither existing IAD approaches nor LVLMs can adequately handle the IAD problem. Data scarcity is the first.

Data Scarcity 123

Data Scarcity Large Language Models Natural Language Processing LLM 123

Marktechpost

AUGUST 3, 2023

First, they proposed an LLM-based approach to generate a music captioning dataset, LP-MusicCaps. Second, they proposed a systemic evaluation scheme for music captions generated by LLMs. The researchers compared this LLM-based caption generator with template-based methods (tag concatenation, prompt template ) and K2C augmentation.

Data Scarcity 52

Data Scarcity Large Language Models BERT Natural Language Processing 52

Marktechpost

JANUARY 28, 2025

Augmentation Augmentation plays a central role in fine-tuning by extending the capabilities of LLMs by incorporating external data or techniques. For example, augmenting an LLM with legal terminology can significantly improve its performance in drafting contracts or summarizing case law.

Large Language Models 115

Large Language Models LLM Data Scarcity Explainability 115

Marktechpost

DECEMBER 10, 2024

Traditionally, addressing these challenges involved relying on human-labeled data or leveraging LLMs as judges to verify trajectories. While LLM-based solutions have shown promise, they face significant limitations, including sensitivity to input prompts, inconsistent outputs from API-based models, and high operational costs.

Machine Learning 87

Machine Learning Data Scarcity LLM Large Language Models 87