This is the fourth in a series of post on Fine-Tuning LLMs, for previous articles check out Five Large Language Models You Can Fine-Tune Today, Fine Tuning LLMs, and Data Labeling and Comparative Analysis of Fine-Tuning Methods

Navigating the world of Large Language Models (LLMs) can be a daunting task, especially when it comes to fine-tuning these models for specific tasks. The jargon can often be overwhelming, and understanding the terminology is the first step towards mastering the art of LLM fine-tuning.

In this blog post, we aim to demystify the lingo around fine-tuning LLMs. We've compiled a comprehensive glossary of terms that you should know when embarking on your LLM tuning journey. This glossary is part of our ongoing series on Fine-Tuning LLMs, and it's designed to equip you with the vocabulary you need to understand and engage in discussions about LLMs.

Whether you're a seasoned data scientist or a beginner in the field of machine learning, this glossary will serve as a handy reference guide. It covers everything from basic terms to more advanced concepts, providing clear and concise definitions that are easy to understand.

So, if you've ever found yourself puzzled by terms like 'transfer learning', 'fine-tuning', or 'active learning', worry no more. Dive into our glossary and arm yourself with the knowledge you need to fine-tune LLMs like a pro.

Out of the box, LLMs can handle general tasks fairly easily. But what do you do when you need to complete tasks that require more specialized knowledge than what LLMs typically have? You'll need to fine-tune the model by training it on your specific use case. This article is an excerpt from our ebook - Essential Guide to Fine-Tuning LLMS. You can download the entire book here.

Glossary of Terms for LLM Tuning

These are terms commonly used in fine-tuning of LLMs.

• Activation Function - A function in a neural network that is used to determine the output of a neuron. It helps to decide whether a particular neuron should be activated or not based on the weighted sum of its inputs.
• Adapter Training - A methodology that enables fine-tuning on a specific task without disrupting the original model's performance on other tasks. This approach involves training lightweight modules that can be integrated into the pre-trained model.
• Attention Mechanism - A mechanism in a neural network that allows the model to focus on certain parts of the input when producing an output. It's a key component of Transformer models.
• Back-Translation - A technique used in data augmentation for NLP tasks. It involves translating a sentence from the original language to a target language and then back to the original language.
• Backpropagation - A method used during training to adjust the model's parameters based on the output's error rate (or loss).
• Batch Size - The number of training examples used in one iteration of model training.
• Batching - A process in machine learning where training data is divided into smaller groups or 'batches'. The model is then trained on each batch separately. This can make the training process more efficient.
• Bias -Variance Tradeoff - A problem in machine learning where increasing the bias will decrease the variance and vice versa. These two types of errors must be balanced for optimal model performance.
• Data Labeling - The process of assigning labels or annotations to data points. These labels give the model the necessary information to learn and make accurate predictions.
• Embeddings are vector representations of a word or phrase in a high-dimensional space. Word embeddings capture the semantic relationships between words.
• Entity - In natural language processing, an entity refers to words or phrases in the text that have a specific meaning or refer to a specific type of information, such as a person's name, a location, a date, etc.
• Epoch - An epoch is a complete pass through the entire training dataset during the training process.
• Fine -Tuning - The process of taking a pre-trained model and adapting it to perform a specific task. This involves adjusting the model's parameters and exposing it to task-specific data.
• Few-Shot Learning - A fine-tuning methodology that allows models to learn from limited examples. It is suitable for scenarios where data is scarce or expensive to obtain.
• GPT (Generative Pretrained Transformer) - A type of transformer-based language prediction model that uses unsupervised learning and can generate paragraphs of text.
• Generalization - The ability of a machine learning model to perform well on unseen data that was not used during the training process.
• Generative AI - Generative AI refers to a subset of artificial intelligence that uses machine learning models to create and generate new content, such as images, text, or music. These models learn patterns from existing data and generate novel, creative outputs, allowing for the generation of realistic and unique content.
• Gradient Descent - An optimization algorithm that minimizes the loss function during training.
• Hyperparameter Tuning - The process of adjusting the hyperparameters of a model to improve its performance.
• Hyperparameters - These are parameters whose values are set before the learning process begins. They determine the model's structure and learning speed and need to be tuned to optimally solve the machine learning problem.
• Large Language Models (LLMs) - These are machine learning models trained on a large amount of text data. They are capable of understanding, generating, and manipulating human language.
• Layer - A collection of neurons that process a set of input data in a neural network. A neural network has three types of layers: input, hidden layer(s), and output.
• Learning Rate - A hyperparameter that determines the step size at each iteration while moving toward a minimum of loss function. It decides how quickly or slowly a machine learning model 'learns.'
• Loss Function - A method of evaluating how well a specific algorithm models the given data. If predictions deviate too much from actual results, loss function would cough up a very large number. Gradually, with the help of some optimization function, the loss function learns to reduce the error in prediction.
• Model Evaluation - The process of determining how well a machine learning model performs. This involves comparing the predictions of the model against actual data.
• Model Generalization - The ability of a model to perform well on unseen data. A model that generalizes well can accurately predict outcomes based on new data it has not been trained on.
• Model Iteration - The process of making repeated passes through the dataset, updating the model parameters with each pass to improve the model's performance.
• Model Optimization - The process of adjusting the parameters and architecture of a model to improve its performance.
• Model Overfitting - A situation where a model learns the training data too well, to the point where it performs poorly on new, unseen data.
• Model Underfitting - A situation where a model is too simple to capture all of the nuances in the data, resulting in poor performance on both the training data and new, unseen data.
• Multi-Task Learning - This involves training a model on multiple tasks simultaneously. This approach improves generalization and performance by leveraging shared representations across different tasks.
• Neuron - The basic unit of computation in a neural network. Each neuron takes inputs, performs some operations, and produces an output.
• Normalization - A pre-processing step that transforms all the text and features into a standard format or scale. This makes the training process more efficient and helps the model learn the patterns in the data more effectively.
• Overfitting - A modeling error occurs when a function is too closely fit to a limited set of data points. Overfitting the model results in poor predictive performance as it makes the model sensitive to high degrees of variation in input data.
• Pre-Trained Models - Models already trained on a large dataset. These models can be used as a starting point for fine-tuning.
• Regularization - A technique used to prevent overfitting by adding a penalty to the loss function based on the complexity of the model.
• Reinforcement Learning from Human Feedback (RLHF)  - RLHF refers to a machine learning technique that combines elements of reinforcement learning and human guidance to train and improve models. RLHF involves utilizing human-generated feedback to guide and optimize the behavior and performance of an AI model.
• Sequential Fine -Tuning - This involves training a model on multiple related tasks one after the other. This approach enables the model to understand nuanced language patterns across various tasks.
• Task-Specific Fine-Tuning - This aims to adapt the pre-trained model to excel at a particular task. This approach requires more data and time but can lead to high performance on the specific task at hand.
• Test Dataset - A subset of the dataset that is strictly used for testing the performance of a trained machine learning model.
• Tokenization - The process of converting text into tokens (smaller pieces) that a machine learning model can understand. These tokens help understand the context or develop numerical representations for machine learning algorithms.
• Transfer Learning - A methodology in fine-tuning where the knowledge gained from one task is utilized to solve a different but related task. This approach reduces the need for extensive data and computational power.
• Transformer Models - A type of model architecture introduced in the paper "Attention is All You Need." It relies entirely on self-attention mechanisms and has been the basis for many state-of-the-art models in NLP, including BERT and GPT.
• Underfitting - A modeling error that occurs when a function is too simple to accurately capture the underlying structure of the data. Underfitting the model results in poor predictive performance as it fails to capture important trends in the data.
• Validation Dataset - A subset of the dataset used to evaluate the model's performance after training but before testing. It provides a 'check' on overfitting and helps in hyperparameter tuning.
• Weights - The parameters in a neural network that transform input data within the network's layers. They are learned and updated during the training process.