Category: Llms
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How Metrics (and LLMs) Can Trick You: A Field Guide to Paradoxes
How Metrics (and LLMs) Can Trick You: A Field Guide to Paradoxes When numbers lie — and your metrics mislead you The post How Metrics (and LLMs) Can Trick You: A Field Guide to Paradoxes appeared first on Towards Data Science. Subha Ganapathi Go to original source
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Retrieval Augmented Classification: Improving Text Classification with External Knowledge
Retrieval Augmented Classification: Improving Text Classification with External Knowledge Text Classification stands as one of the most basic yet most important applications of natural language processing. It has a vital role in many real-world applications that go from filtering unwanted emails like spam, detecting product categories or classifying user intent in a chat-bot application. The…
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Enhancing RAG: Beyond Vanilla Approaches
Enhancing RAG: Beyond Vanilla Approaches Retrieval-Augmented Generation (RAG) is a powerful technique that enhances language models by incorporating external information retrieval mechanisms. While standard RAG implementations improve response relevance, they often struggle in complex retrieval scenarios. This article explores the limitations of a vanilla RAG setup and introduces advanced techniques to enhance its accuracy and…
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6 Common LLM Customization Strategies Briefly Explained
6 Common LLM Customization Strategies Briefly Explained Why Customize LLMs? Large Language Models (Llms) are deep learning models pre-trained based on self-supervised learning, requiring a vast amount of resources on training data, training time and holding a large number of parameters. LLM have revolutionized natural language processing especially in the last 2 years, demonstrating remarkable…