Tags: natural language processing*

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  1. This article provides a comprehensive guide on the basics of BERT (Bidirectional Encoder Representations from Transformers) models. It covers the architecture, use cases, and practical implementations, helping readers understand how to leverage BERT for natural language processing tasks.
  2. The article provides a comprehensive introduction to large language models (LLMs), explaining their purpose, how they function, and their applications. It covers various types of LLMs, including general-purpose and task-specific models, and discusses the distinction between closed-source and open-source LLMs. The article also explores the ethical considerations of building and using LLMs and the future possibilities for these models.
  3. This paper presents a detailed vocabulary of 33 terms and a taxonomy of 58 LLM prompting techniques, along with guidelines for prompt engineering and a meta-analysis of natural language prefix-prompting, serving as the most comprehensive survey on prompt engineering to date.
  4. A tutorial on using LLM for text classification, addressing common challenges and providing practical tips to improve accuracy and usability.
  5. An article discussing the use of embeddings in natural language processing, focusing on comparing open source and closed source embedding models for semantic search, including techniques like clustering and re-ranking.
  6. This blog post explores applying the original ELIZA chatbot, a pioneering natural language processing program, in a way similar to modern large language models (LLMs) by using it to carry on an educational conversation about George Orwell's 'Animal Farm'.
  7. This article discusses Re2, a prompting technique that enhances reasoning in Large Language Models (LLMs) by re-reading the input twice. It improves understanding and reasoning capabilities, leading to better performance in various benchmarks.
  8. This article explains BERT, a language model designed to understand text rather than generate it. It discusses the transformer architecture BERT is based on and provides a step-by-step guide to building and training a BERT model for sentiment analysis.
  9. This article explores the use of word2vec and GloVe algorithms for concept analysis within text corpora. It discusses the history of word2vec, its ability to perform semantic arithmetic, and compares it with the GloVe algorithm.
  10. This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. RAG systems combine information retrieval with generative models to provide accurate and contextually rich responses.

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