A surprising experiment to show that the devil is in the details
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
Learn how to summarize large documents using LangChain and OpenAI, addressing contextual limits and cost effectively. This tutorial covers text preprocessing, semantic chunking, K-means clustering, and document summarization.
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.
ColBERT is a new way of scoring passage relevance using a BERT language model that substantially solves the problems with dense passage retrieval.
apt install tesseract-ocr-fra
Andrej Karpathy's recommended paper reading list, covering various aspects of Language Models (LLMs), including attention mechanisms, unsupervised multi-task learning (GPT-2), instruction-following language models (InstructGPT), LLaMA, reinforcement learning from human feedback (RLAIF), and early experiments of GPT-4, offering insights into significant research developments in LLM and their role in AI landscape, benefiting both novice and experienced AI enthusiasts.
