Google DeepMind research reveals a fundamental architectural limitation in Retrieval-Augmented Generation (RAG) systems related to fixed-size embeddings. The research demonstrates that retrieval performance degrades as database size increases, with theoretical limits based on embedding dimensionality. They introduce the LIMIT benchmark to empirically test these limitations and suggest alternatives like cross-encoders, multi-vector models, and sparse models.
The author discusses a shift in approach to clustering mixed data, advocating for starting with the simpler Gower distance metric before resorting to more complex embedding techniques like UMAP. They introduce 'Gower Express', an optimized and accelerated implementation of Gower.
This article explores how different decision tree hyperparameters affect performance and visual structure, using scikit-learn's DecisionTreeRegressor and the California housing dataset. It examines the impact of max_depth, ccp_alpha, min_samples_split, min_samples_leaf, and max_leaf_nodes, and demonstrates the use of cross-validation and BayesSearchCV for optimal hyperparameter tuning.
This article explores the impact of hyperparameters on random forests, both in terms of performance and visual representation. It compares the performance of a default random forest with tuned decision trees and examines the effects of various hyperparameters like `n_estimators`, `max_depth`, and `ccp_alpha` using visualizations of individual trees, predictions, and errors.
Extracting structured information effectively and accurately from long unstructured text with LangExtract and LLMs. This article explores Google’s LangExtract framework and its open-source LLM, Gemma 3, demonstrating how to parse an insurance policy to surface details like exclusions.
This tutorial explores implementing the LLM Arena-as-a-Judge approach to evaluate large language model outputs using head-to-head comparisons. It demonstrates using OpenAI’s GPT-4.1 and Gemini 2.5 Pro, judged by GPT-5, in a customer support scenario.
An Apple study shows that large language models (LLMs) can improve performance by using a checklist-based reinforcement learning scheme, similar to a simple productivity trick of checking one's work.
This article explains how derivatives, gradients, Jacobians, and Hessians fit together and shows examples of what they are used for, including optimization and rendering.
This page details the command-line utility for the Embedding Atlas, a tool for exploring large text datasets with metadata. It covers installation, data loading (local and Hugging Face), visualization of embeddings using SentenceTransformers and UMAP, and usage instructions with available options.
Build, enrich, and transform datasets using AI models with no code. This repository provides the source code for Hugging Face AI Sheets, an open-source tool for dataset manipulation using AI.
