klotz: natural language processing*

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  1. A method that uses instruction tuning to adapt LLMs for knowledge-intensive tasks. RankRAG simultaneously trains the models for context ranking and answer generation, enhancing their retrieval-augmented generation (RAG) capabilities.
  2. NVIDIA and Georgia Tech researchers introduce RankRAG, a novel framework instruction-tuning a single LLM for top-k context ranking and answer generation. Aiming to improve RAG systems, it enhances context relevance assessment and answer generation.
  3. This guide explains how to build and use knowledge graphs with R2R. It covers setup, basic example, construction, navigation, querying, visualization, and advanced examples.
  4. This article provides a beginner's guide on using Hugging Face Transformers for text summarization. It explains what text summarization is, its uses, and how it can be performed using extractive and abstractive summarization techniques. The article also provides a simple code example using the Hugging Face pipeline for text summarization.
  5. Case study on measuring context relevance in retrieval-augmented generation systems using Ragas, TruLens, and DeepEval. Develop practical strategies to evaluate the accuracy and relevance of generated context.
  6. This tutorial covers fine-tuning BERT for sentiment analysis using Hugging Face Transformers. Learn to prepare data, set up environment, train and evaluate the model, and make predictions.
  7. • A beginner's guide to understanding Hugging Face Transformers, a library that provides access to thousands of pre-trained transformer models for natural language processing, computer vision, and more.
    • The guide covers the basics of Hugging Face Transformers, including what it is, how it works, and how to use it with a simple example of running Microsoft's Phi-2 LLM in a notebook
    • The guide is designed for non-technical individuals who want to understand open-source machine learning without prior knowledge of Python or machine learning.
  8. This article explores the rise of foundation models in time series forecasting. The authors discuss the increasing success of these approaches in areas such as natural language processing and their potential impact on the field of predictive analytics.
  9. Quivr is an open-source RAG framework and a robust AI assistant that helps you manage and interact with information, reducing the burden of information overload. It integrates with all your files and programs, making it easy to find and analyze your data in one place.
  10. The paper proposes a two-phase framework called TnT-LLM to automate the process of end-to-end label generation and assignment for text mining using large language models, where LLMs produce and refine a label taxonomy iteratively using a zero-shot, multi-stage reasoning approach, and are used as data labelers to yield training samples for lightweight supervised classifiers. The framework is applied to the analysis of user intent and conversational domain for Bing Copilot, achieving accurate and relevant label taxonomies and a favorable balance between accuracy and efficiency for classification at scale.

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