ROUGE (Recall-Oriented Understudy for Gisting Evaluation) is a set of evaluation metrics widely used in natural language processing (NLP) to assess automatic summarization, machine translation, and text generation systems. It measures the overlap between machine-generated outputs and human-written references, focusing...

RNN (Recurrent Neural Network) is a type of neural network designed to process sequential data. Unlike traditional feedforward networks, RNNs maintain a hidden state that captures information about previous inputs, making them ideal for tasks involving time-series, language, or any...

RLHF (Reinforcement Learning from Human Feedback) is a technique used to fine-tune machine learning models, particularly large language models, using human feedback as a guiding signal. Rather than relying solely on static datasets, RLHF incorporates human preferences to train models...

Synthetic Data refers to artificially generated information that imitates real-world data. It is created using algorithms or simulations instead of being collected from actual events. Consequently, synthetic data enables safe experimentation, improves model strength, and protects privacy without sacrificing data...

Regression is a fundamental technique in machine learning and statistics. It models the relationship between a dependent variable and one or more independent variables. Regression analysis predicts continuous outcomes and shows how changes in input variables influence the target variable....

Red Team refers to a group of experts who simulate real-world attacks to test the security, reliability, or safety of systems, organizations, or AI models. Their goal is to identify vulnerabilities and weaknesses before adversaries can exploit them. In AI...

Rectified Flow is a novel method in generative modeling that simplifies and speeds up the generation of complex data, such as images or 3D shapes. Unlike traditional diffusion models that gradually denoise a sample over many steps, rectified flow directly...

RAGAS (Retrieval-Augmented Generation Assessment System) is an evaluation framework designed to measure the performance of retrieval-augmented generation (RAG) systems. RAGAS focuses on how effectively a system retrieves relevant information and generates coherent, factual responses based on that information, addressing both...

RAG (Retrieval Augmented Generation) is a technique that enhances generative AI models—such as large language models—by integrating external, domain-specific knowledge sources into the generation process. Rather than relying solely on patterns learned from pre-training, RAG dynamically fetches relevant information from...