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.
This article discusses Retrieval-Augmented Generation (RAG) models, a new approach that addresses the limitations of traditional models in knowledge-intensive Natural Language Processing (NLP) tasks. RAG models combine parametric memory from pre-trained seq2seq models with non-parametric memory from a dense vector index of Wikipedia, enabling dynamic knowledge access and integration.
This article explains how to use the Sentence Transformers library to finetune and train embedding models for a variety of applications, such as retrieval augmented generation, semantic search, and semantic textual similarity. It covers the training components, dataset format, loss function, training arguments, evaluators, and trainer.
"The paper introduces a technique called LoReFT (Low-rank Linear Subspace ReFT). Similar to LoRA (Low Rank Adaptation), it uses low-rank approximations to intervene on hidden representations. It shows that linear subspaces contain rich semantics that can be manipulated to steer model behaviors."
Learn about function calling in Large Language Models (LLMs) and the list of commercial and open source LLMs suitable for function calling.
Researchers from NYU Tandon School of Engineering investigated whether modern natural language processing systems could solve the daily Connections puzzles from The New York Times. The results showed that while all the AI systems could solve some of the puzzles, they struggled overall.
This article provides a beginner-friendly introduction to Large Language Models (LLMs) and explains the key concepts in a clear and organized way.
LangChain has many advanced retrieval methods to help address these challenges. (1) Multi representation indexing: Create a document representation (like a summary) that is well-suited for retrieval (read about this using the Multi Vector Retriever in a blog post from last week). (2) Query transformation: in this post, we'll review a few approaches to transform humans questions in order to improve retrieval. (3) Query construction: convert human question into a particular query syntax or language, which will be covered in a future post
This article explores how to boost the performance of small language models by using supervision from larger ones through knowledge distillation. The article provides a step-by-step guide on how to distill knowledge from a teacher model (LLama 2–70B) to a student model (Tiny-LLama) using unlabeled in-domain data and targeted prompting.
This article explores the application of XML Schema in AI systems and prompts. XML Schema provides a structured way to describe and validate data, making it an essential tool for AI systems that deal with data. The author discusses how XML Schema can be used to create and manage data in AI applications, such as speech recognition and natural language processing. The article also covers the benefits of using XML Schema in AI systems, including improved data consistency, interoperability, and security. Lastly, the author provides some examples of XML Schema usage in AI systems and discusses the future of XML Schema in AI technology.
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