This article discusses the emergence of large language models in the field of natural language processing (NLP) and the innovative ways in which they are being used. It highlights various applications such as text-to-image and text-to-speech, as well as techniques like prompt engineering and knowledge graph augmentation. The article also mentions other recent standout articles on topics like data visualization and customer lifetime-value forecasting.
The Creative, Occasionally Messy World of Textual Data
For several years, natural language processing (NLP) has been used to leverage textual data for prediction, classification, and recommendation tools. However, the rise of large language models has opened up new possibilities in this field. Here are some highlights:
Lost in DALL-E 3 Translation
Explore the discrepancies in model performance for users working in under-resourced languages and the biases that seep through into generated images.
How to Convert Any Text Into a Graph of Concepts
Learn how to transform a text corpus into a Graph of Concepts (GC) and visualize it to detect patterns and gain meaningful insights.
RAG: How to Talk to Your Data
Discover a practical workflow for analyzing customer feedback using ChatGPT.
FastSpeech: Paper Overview & Implementation
Understand how text-to-speech tools work and how transformers are employed to improve their performance.
Unlocking the Power of Text Data with LLMs
Get started with cutting-edge text-data techniques through a step-by-step guide, from downloading data to working with GPT-3 and analyzing results.
A Universal Roadmap for Prompt Engineering: The Contextual Scaffolds Framework (CSF)
Learn about the contextual scaffolds framework (CSF), a mental model for effective prompt engineering.
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