AI (Artificial Intelligence)

AI refers to the simulation of human intelligence in machines, allowing them to perform tasks that typically require human intelligence, such as understanding natural language, recognizing patterns, and making decisions.

GPT (Generative Pre-trained Transformer)

GPT is a type of LLM developed by OpenAI. It uses a transformer architecture and is trained on a large corpus of text data to generate human-like text based on prompts or inputs.

LLM (Large Language Model)

LLMs are AI models capable of processing and generating human-like text. They are trained on vast amounts of text data and can perform tasks such as text generation, language translation, sentiment analysis, and more.

NLP (Natural Language Processing)

NLP is a subfield of AI focused on enabling computers to understand, interpret, and generate human language. It encompasses tasks such as text classification, sentiment analysis, machine translation, and text generation.

Fine-tuning

Fine-tuning refers to the process of taking a pre-trained neural network model (such as a GPT model) and further training it on a specific task or domain with a smaller, task-specific dataset. This process allows the model to adapt its parameters to perform better on the target task.

Hallucination

The generation of text that is not grounded in reality or lacks coherence. Hallucination occurs when a language model produces output that is unrelated to the input prompt or deviates significantly from the expected context.

BERT (Bidirectional Encoder Representations from Transformers)

BERT is another popular transformer-based LLM developed by Google. It is pre-trained on a large corpus of text data and has been shown to achieve state-of-the-art performance on various NLP tasks.

Transfer Learning

Transfer learning is a machine learning technique where a model trained on one task is leveraged to perform a different but related task. In the context of LLMs, pre-trained models like GPT and BERT are often fine-tuned on specific tasks using transfer learning.

Attention Mechanism

An attention mechanism is a component of the transformer architecture that allows the model to focus on relevant parts of the input sequence when making predictions. It helps improve the model's performance on tasks that involve long-range dependencies, such as language understanding and translation.

Tokenization

Tokenization is the process of breaking down text into smaller units called tokens, which could be words, subwords, or characters. Tokenization is a crucial preprocessing step in NLP tasks and is essential for feeding text data into neural network models like LLMs.

Data augmentation

Techniques used to increase the diversity and size of the training dataset, often improving model performance. Data augmentation methods include adding noise, rotating, flipping, or cropping input data to create new samples.

Validation set

A portion of the dataset used to tune hyperparameters and evaluate model performance during training. The validation set helps prevent overfitting by providing an independent dataset for assessing the generalization performance of the model.

Test set

A separate portion of the dataset used to evaluate the final performance of the model after training. The test set provides an unbiased evaluation of the model's ability to generalize to unseen data.

Overfitting

When a model learns to perform well on the training data but fails to generalize to unseen data due to memorization of noise or irrelevant patterns. Overfitting occurs when the model captures noise or outliers in the training data, leading to poor performance on new data.

Underfitting

When a model fails to capture the underlying patterns in the data, resulting in poor performance on both training and test sets. Underfitting occurs when the model is too simple to capture the complexities of the data, leading to high bias and low predictive power.

Bias

Systematic errors or prejudices in the LLM's predictions, often influenced by the characteristics of the training data. Bias can arise from imbalanced datasets, sampling errors, or the inherent limitations of the model architecture.

Variance

The amount by which the model's predictions vary across different training datasets, often indicating sensitivity to small changes in the data. Variance measures the model's sensitivity to fluctuations in the training data and its ability to generalize to new data.