The article explains semantic text chunking, a technique for automatically grouping similar pieces of text to be used in pre-processing stages for Retrieval Augmented Generation (RAG) or similar applications. It uses visualizations to understand the chunking process and explores extensions involving clustering and LLM-powered labeling.
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.
This article provides a comprehensive guide on fine-tuning the Llama 3.1 language model using Unsloth for efficient parameter-efficient training. It covers concepts like supervised fine-tuning, LoRA, QLoRA, and practical steps for training on a high-quality dataset.
This article provides a comparative analysis of popular embedding libraries for generative AI, evaluating their strengths, limitations, and suitability for different use cases.
This paper surveys different prompt engineering techniques used to improve the performance of large language models on various Natural Language Processing (NLP) tasks. It categorizes these techniques by NLP task, highlights their performance on different datasets, and discusses state-of-the-art methods for specific datasets. The survey covers 44 research papers exploring 39 prompting methods across 29 NLP tasks.
A Github Gist containing a Python script for text classification using the TxTail API
The article discusses the integration of Large Language Models (LLMs) and search engines, exploring two themes: Search4LLM, which focuses on enhancing LLMs using search engines, and LLM4Search, which looks at improving search engines with LLMs.
A research team introduces Super Tiny Language Models (STLMs) to address the resource-intensive nature of large language models, providing high performance with significantly reduced parameter counts.
This article is part of a series titled ‘LLMs from Scratch’, a complete guide to understanding and building Large Language Models (LLMs). In this article, we discuss the self-attention mechanism and how it is used by transformers to create rich and context-aware transformer embeddings.
The Self-Attention mechanism is used to add context to learned embeddings, which are vectors representing each word in the input sequence. The process involves the following steps:
1. Learned Embeddings: These are the initial vector representations of words, learned during the training phase. The weights matrix, storing the learned embeddings, is stored in the first linear layer of the Transformer architecture.
2. Positional Encoding: This step adds positional information to the learned embeddings. Positional information helps the model understand the order of the words in the input sequence, as transformers process all words in parallel, and without this information, they would lose the order of the words.
3. Self-Attention: The core of the Self-Attention mechanism is to update the learned embeddings with context from the surrounding words in the input sequence. This mechanism determines which words provide context to other words, and this contextual information is used to produce the final contextualized embeddings.
The Self-Attention mechanism is used to add context to learned embeddings, which are vectors representing each word in the input sequence. The process involves the following steps:
1. Learned Embeddings: These are the initial vector representations of words, learned during the training phase. The weights matrix, storing the learned embeddings, is stored in the first linear layer of the Transformer architecture.
2. Positional Encoding: This step adds positional information to the learned embeddings. Positional information helps the model understand the order of the words in the input sequence, as transformers process all words in parallel, and without this information, they would lose the order of the words.
3. Self-Attention: The core of the Self-Attention mechanism is to update the learned embeddings with context from the surrounding words in the input sequence. This mechanism determines which words provide context to other words, and this contextual information is used to produce the final contextualized embeddings.
In this article, we will explore various aspects of BERT, including the landscape at the time of its creation, a detailed breakdown of the model architecture, and writing a task-agnostic fine-tuning pipeline, which we demonstrated using sentiment analysis. Despite being one of the earliest LLMs, BERT has remained relevant even today, and continues to find applications in both research and industry.
