Tokenization is the process of breaking down text into smaller units, called tokens. These tokens can be words, subwords, or even individual characters, depending on the specific approach and the language involved. Tokenization is a fundamental step in natural language...

TensorFlow is an open-source machine learning framework developed by Google Brain. Designed to simplify the building, training, and deployment of machine learning models, TensorFlow provides a wide range of tools and APIs for handling everything from linear regression to advanced...

Synthetic data refers to artificially generated information that replicates the statistical properties and patterns found in real-world datasets, without directly exposing sensitive or private content. In the context of large language model (LLM) evaluation, synthetic data can be especially valuable....

Supervised Learning is a machine learning paradigm where models are trained using labeled examples. Each training instance comes with input features and an associated correct output (label), enabling the model to learn the relationship between inputs and outputs. Over time,...

An sLLM (Specialized Large Language Model) is a large language model that has been tailored or fine-tuned for a specific domain, task, or use case. Unlike general-purpose LLMs, which are trained on vast and diverse datasets, sLLMs focus on a...

The Segment Anything Model (SAM) is a powerful AI tool designed to identify and isolate objects within images. Developed by Meta AI, SAM has been trained on a vast and diverse set of images, enabling it to “segment” virtually any...

SaaS (Software as a Service) is a cloud computing model where users access software applications over the internet without installing or maintaining them on their own hardware. Instead of purchasing and managing licenses or handling complex installations, organizations and individuals...

ROUGE (Recall-Oriented Understudy for Gisting Evaluation) is a set of metrics used to evaluate the quality of automatically generated summaries by comparing them to human-written reference summaries. Instead of assessing a summary’s correctness or coherence directly, ROUGE measures how many...

RNN (Recurrent Neural Network) is a type of neural architecture designed to handle sequential data, such as time series, text, or speech. Unlike traditional neural networks, RNNs have loops that allow information to persist through multiple steps. This makes them...

RLHF (Reinforcement Learning from Human Feedback) is a technique that incorporates human judgments into the training loop of reinforcement learning models. Instead of using only automated reward signals, RLHF involves having humans provide guidance—such as ranking outputs or offering corrective...