Large Language Model (LLM) is a type of AI model designed to process, understand, and generate human-like text. LLMs are trained on vast amounts of text data and use transformer-based architectures to excel in a wide range of natural language...

Latent space refers to the abstract, multidimensional space where data is represented in a compressed or encoded form. It is created by machine learning models, such as neural networks or autoencoders, to capture the most essential features of data, removing...

LangChain is a framework designed for building applications powered by Large Language Models (LLMs) that integrate external data sources, memory, and complex workflows. It allows developers to create sophisticated AI applications by chaining multiple LLM queries together and incorporating retrieval-based...

KorNAT (Korean National Alignment Test) is a benchmark dataset developed to assess the alignment of Large Language Models (LLMs) with South Korean social values and common knowledge. It evaluates models across two primary dimensions: Social Value Alignment: Measures how well...

A knowledge graph is a structured representation of information where entities (nodes) are connected by relationships (edges) to form a graph. It organizes and links data in a way that allows machines to understand, retrieve, and infer new information efficiently,...

Keras is an open-source deep learning framework designed to simplify the creation and deployment of neural networks. It provides an intuitive, user-friendly interface for building and training models while offering compatibility with powerful backends like TensorFlow, Theano, and Microsoft Cognitive...

Instruction Tuning is the process of refining large language models (LLMs) so that they better follow user-provided instructions or prompts. Rather than simply predicting the next word based on statistical patterns, instruction-tuned models are trained on curated datasets of prompts...

Human alignment refers to the process of designing and training AI systems to act in ways that align with human intentions, values, and ethical principles. The goal is to ensure that AI systems perform tasks safely, reliably, and in accordance...

In the context of AI and machine learning, hallucination refers to the generation of outputs by an AI model that are incorrect, fabricated, or nonsensical, despite appearing plausible or confident. This phenomenon is common in generative models, especially large language...

Ground truth refers to the accurate, verified data used as a benchmark or standard to evaluate and train machine learning models. It represents the true state or label of data, often collected through expert annotation, observation, or measurement, and is...