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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 PromptEngineeringPromptengineering is crucial for guiding LLMs to generate high-quality, relevant synthetic data.
However, acquiring such datasets presents significant challenges, including datascarcity, 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.
Multilingual promptengineering is the art and science of creating clear and precise instructions for AI models that understand and respond in multiple languages. This article discusses the difficulties that multilingual promptengineering encounters and solutions to those difficulties.
We provide an overview of key generative AI approaches, including promptengineering, 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.
Promptengineering : the provided prompt plays a crucial role, especially when dealing with compound nouns. By using “car lamp” as a prompt, we are very likely to detect cars instead of car lamps. The first concept is promptengineering. Text: The model accepts text prompts. Source: own study.
Strategy and Data: Non-top-performers highlight strategizing (24%), talent availability (21%), and datascarcity (18%) as their leading challenges. Currently, getting LLMs to function as agents requires a lot of messy promptengineering, and the technology seems quite finicky and unready for use in production.”
Promptengineering : the provided prompt plays a crucial role, especially when dealing with compound nouns. By using car lamp as a prompt, we are very likely to detect cars instead of car lamps. The first concept is promptengineering. Text: The model accepts text prompts. Source: own study.
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 datascarcity, bias, and noise can significantly impact model performance.
Techniques like Uprise and DaSLaM use lightweight retrievers or small models to optimize prompts, break down complex problems, or generate pseudo labels. These methods significantly reduce manual promptengineering efforts and improve performance across various reasoning tasks.
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