Demystifying AI & LLMs

Keywords of AI & LLMs

Understanding keywords related to Artificial Intelligence (AI) and Large Language Models (LLMs) is crucial for grasping their functionality, applications, and development. Here are some essential keywords and concepts:

  1. Large Language Models (LLMs):
  • GPT (Generative Pre-trained Transformer): A type of LLM developed by OpenAI, known for its ability to generate human-like text.
  • BERT (Bidirectional Encoder Representations from Transformers): An LLM developed by Google, designed to understand the context of words in search queries.
  • Transformer: The neural network architecture that underpins most modern LLMs, including GPT and BERT, known for its efficiency in handling sequential data like text.
  1. Training:
  • Pre-training: The initial phase where the model learns from a large corpus of text data in an unsupervised manner.
  • Fine-tuning: Adjusting a pre-trained model on a smaller, task-specific dataset to improve performance on that specific task.
  • Backpropagation: The process of adjusting the weights of a neural network based on the error rate of the output compared to the expected outcome.
  1. Architecture:
  • Neural Networks: Computing systems inspired by the biological neural networks of animal brains, essential for learning patterns in data.
  • Attention Mechanism: A component of the Transformer architecture that allows the model to focus on relevant parts of the input when generating an output.
  • Parameters: The weights and biases in a neural network that are learned from the training data and used to make predictions.
  1. Natural Language Processing (NLP):
  • Tokenization: Breaking down text into smaller units like words or subwords that the model can process.
  • Embedding: Representing words or tokens as vectors in a continuous vector space.
  • Sequence-to-Sequence (Seq2Seq): A model architecture used for tasks where the input and output are both sequences, such as translation.
  1. Applications:
  • Chatbots: AI systems designed to simulate conversation with human users.
  • Text Generation: The creation of new text based on a given input, used in applications like story writing or code generation.
  • Machine Translation: Automatically translating text from one language to another.
  1. Ethics and Bias:
  • Bias: The tendency of an AI system to make decisions based on prejudiced or unbalanced data.
  • Fairness: Ensuring AI systems make impartial and just decisions.
  • Explainability: The ability to explain how an AI model arrives at a decision, which is critical for trust and transparency.
  1. Evaluation:
  • Perplexity: A measure of how well a language model predicts a sample; lower perplexity indicates better performance.
  • BLEU (Bilingual Evaluation Understudy): A metric for evaluating the quality of text which has been machine-translated from one language to another.
  • Accuracy, Precision, Recall, F1 Score: Standard metrics for evaluating the performance of classification models.

Understanding these keywords provides a solid foundation for diving deeper into the field of AI and LLMs, enabling a better grasp of both the theoretical and practical aspects of these technologies.

Image credit

The Original Benny C, CC BY-SA 4.0, via Wikimedia Commons