KDnuggets·2yA Simple to Implement End-to-End Project with HuggingFace
Create an end-to-end project using a pre-trained Hugging Face model for sentiment analysis. This guide details how to deploy the model with FastAPI, build an API endpoint, and use Docker to containerize the application for easy deployment.
GoPenAI·2yMastering RAG Chunking Techniques for Enhanced Document Processing
Dividing large documents into smaller segments, known as chunking, is crucial for optimizing Retrieval-Augmented Generation (RAG) systems. These systems combine retrieval-based and generative approaches to improve output quality. Various chunking methods, such as sentence, token, and regex splitters, are discussed with a focus on a novel technique using sentence embeddings to identify topic changes. This new method ensures that each chunk represents a coherent topic, enhancing the system's ability to generate accurate and relevant responses.
Machine Learning News·2yPerplexica: The Open-Source Solution Replicating Billion Dollar Perplexity for AI Search Tools
Perplexica is an efficient, transparent, and open-source search tool that solves the problems of inadequate search relevance and privacy issues in traditional and proprietary AI-powered search engines.
Substack·2ySummarization and the Evolution of LLMs
This post explores the evolution of large language models (LLMs) and their impact on natural language processing research, with a focus on summarization. It discusses the basics of summarization, types of summarization techniques, and the process of writing summaries with LLMs. The post also covers popular datasets and evaluation metrics for summarization. Additionally, it highlights the use of human feedback and preference tuning to train LLMs for better summarization. Finally, it examines the impact of LLMs, particularly GPT-3, on news summarization and opinion summarization.
Community Picks·2yLet's reproduce GPT-2 (124M)
This post discusses the process of reproducing the GPT-2 (124M) model, including loading the weights, implementing the model from scratch, and generating text. It also introduces the Tiny Shakespeare dataset and shows how to use it for training. The author demonstrates how to calculate loss and perform optimization using PyTorch.
Towards AI·2yThe Rise of Vector Databases: Understanding Vector Search and RAG Pipeline
This post explains the fundamentals of vectors and vector databases, as well as the implementation of vector searches and Retrieval-Augmented Generation (RAG) using Qdrant Vector Database. It covers the applications of vector databases in NLP, recommendation engines, and image/video searches. The post also provides a step-by-step guide on how to perform vector searches using Qdrant database and how RAG can be used to improve the accuracy of language models.
Towards Data Science·2yUnderstanding Transformers
Transformers, introduced in 2017, revolutionized sequence transduction models by relying entirely on the attention mechanism and allowing for parallel processing, which significantly improved training efficiency and long-term dependency handling compared to previous models like RNNs, LSTMs, and CNNs. Key components of a transformer include tokenization, embedding, the attention mechanism, the encoder, and the decoder. GPT models, which stem from transformers, focus on generative tasks and omit the encoder stack, demonstrating high effectiveness in tasks like generating text after being pre-trained on large corpora of text.
Towards AI·2yA Complete Guide to RAG
Retrieval-Augmented Generation (RAG) is a powerful technique that combines a strong existing language model with a retrieval system to efficiently handle company-specific information. Unlike retraining models, which is often impractical, RAG leverages a vector-based search to fetch relevant company documents and uses a language model to generate answers. This approach involves a retriever for searching and a generator for response crafting, significantly improving efficiency. Advanced techniques like RAG Fusion, Cross and Bi-Encoders, and ensemble retrievers enhance the system's accuracy and relevance. Tuning methods such as RELP and FLARE further optimize model performance, making RAG an effective solution for handling unstructured data and varying queries.
The New Stack·2yAI Agents: Key Concepts and How They Overcome LLM Limitations
AI agents augment LLMs by incorporating memory, enabling asynchronous processing, fact-checking and real-time information access, enhancing mathematical capabilities, ensuring consistent output formatting, and creating persona-driven interactions.
The New Stack·2yRAG vs. Fine-Tuning Models: What’s the Right Approach?
Retrieval-Augmented Generation (RAG) retrieves relevant documents to generate contextually accurate responses, ideal for dynamic environments like enterprise search and customer support. Fine-tuning involves training a model on specific datasets for specialized tasks, ensuring consistency and improved performance for targeted applications. Choosing between RAG and fine-tuning depends on the need for adaptability or task-specific expertise.
GoPenAI·2yCan 2 LLM calls boost your RAG’s performance?
Building a real-world Retrieval Augmented Generation (RAG) system for handling company reports presents unique challenges and solutions. Initially struggling with generating accurate responses from unstructured data, the author experimented with different models and retrieval methods. Ultimately, using a smaller in-house LLM, Mistral 7B, for both generating metadata and crafting responses, outperformed even a powerful LLM like GPT-4. The key takeaway is the effective use of metadata filters and strategic application of smaller LLMs for enhanced performance.
KDnuggets·2yThe Ultimate Guide to Approach LLMs
Learn about the basics of Large Language Models and how to approach learning new technological advancements. Discover the challenges that come with language understanding and contextual learning. Find tips for business leaders to leverage AI technology and build trust in its capabilities.
GoPenAI·2yCaching in LLM-Based Applications
Caching improves the performance and cost-efficiency of LLM-based applications by storing frequently accessed data. Standard caching saves prompts and their responses in a database but struggles with similar prompts being processed separately. Semantic caching addresses this by performing similarity searches between new and cached prompts, returning cached responses when appropriate. Implementing these caching techniques can significantly enhance the efficiency, responsiveness, and cost-effectiveness of applications.
Machine Learning News·2yTopicGPT: A Prompt-based AI Framework that Uses Large Language Models (LLMs) to Uncover Latent Topics in a Text Collection
TopicGPT is a new AI framework that leverages large language models to generate and refine latent topics in text collections, addressing limitations of traditional methods like LDA. It uses prompt-based topic generation and assignment, offering higher-quality and more interpretable topics. Evaluations on Wikipedia articles and Congressional bills show that TopicGPT achieves better alignment with human annotations, making it a valuable tool for content analysis.
Machine Learning News·2yMeet Tsinghua University’s GLM-4-9B-Chat-1M: An Outstanding Language Model Challenging GPT 4V, Gemini Pro (on vision), Mistral and Llama 3 8B
Tsinghua University's GLM-4 9B is a powerful language model that outperforms GPT-4 and Gemini. It supports multi-round dialogue, code execution, web browsing, and more. GLM-4 9B has a versatile architecture, excels in vision tasks, and surpasses existing models in overall accuracy. It presents opportunities in natural language processing, computer vision, and code generation. The release of GLM-4 9B marks a milestone in language models and sets a new benchmark for open-source models.
Machine Learning News·2yHallucination in Large Language Models (LLMs) and Its Causes
Large language models (LLMs) like Llama, PaLM, and GPT-4 have advanced text understanding and generation in natural language processing (NLP). However, LLMs are prone to producing hallucinations, which are factually incorrect or inconsistent content. Hallucinations in LLMs can be categorized into factuality hallucinations and faithfulness hallucinations. The causes of hallucinations span data-related, training-related, and inference-related factors. Mitigation strategies for hallucinations include enhancing data quality, improving training processes, and refining decoding techniques.
GoPenAI·2yBuilding a Document Summarization Web App with OpenAI’s LLM
Learn to build a document summarization web app by leveraging OpenAI's Large Language Model (LLM) and Streamlit. This guide covers creating a front-end for file uploads, back-end processing with helper functions, generating embeddings, and summarizing PDFs. The approach can be scaled to multiple file types and integrated into existing applications, providing valuable use cases for LLMs in industry.
Collections·2yUnderstanding Retrieval-Augmented Generation (RAG): Enhancements, Applications, and Evaluation
Large Language Models (LLMs) have advanced NLP but often struggle with maintaining accuracy and relevance in rapidly changing environments. Retrieval-Augmented Generation (RAG) and its advanced variant, Agent-Based RAG (Agentic RAG), address this by incorporating real-time information retrieval and intelligent agents. RAG dynamically retrieves and integrates external data to enhance LLMs without retraining. Agentic RAG introduces specialized agents coordinated by a meta-agent for improved task performance, scalability, and fault tolerance. Advanced RAG techniques like PlanRAG enhance decision-making, and RAG systems are already improving applications across various industries like healthcare and legal.
neo4j·2yUnleashing the Power of NLP with LlamaIndex and Neo4j: A Starter Kit
Unlock the true potential of natural language processing with the LlamaIndex Neo4j Integration Starter Kit. Learn how to store and query documents, utilize LlamaIndex's powerful indexing capabilities, and leverage the graph database querying of Neo4j. The starter kit also includes a FastAPI application for interactive NLP applications.
Stack Overflow Blog·2yExplaining generative language models to (almost) anyone
Generative AI has gained significant attention, making it crucial for researchers and engineers to communicate its nuances clearly. Generative language models use the transformer architecture, self-supervised learning for pretraining, and alignment techniques to meet human expectations. Understanding these components helps demystify AI and prevents public skepticism and overly-restrictive regulations.
Stack Overflow Blog·2yBreaking up is hard to do: Chunking in RAG applications
Chunking is an important aspect in retrieval-augmented generation (RAG) systems. The size of the chunked data affects the specificity and context of the information retrieved. Common chunking strategies include fixed sizes, random chunk sizes, sliding windows, context-aware chunking, and adaptive chunking.
