LLM Parameters: Understanding the Building Blocks of Large Language Models

Large language models (LLMs) such as ChatGPT and Llama use machine learning to recognize relationships between words, allowing them to power everything from chatbots to search engines. These models rely on billions of parameters assigned to data points that determine how they process and predict data.

Many sectors, including health care, finance, and customer service, use LLMs. Understanding how parameters impact performance can be helpful while working with these models. Learn more about LLM parameters, how they influence the predictions artificial intelligence (AI) tools make, and why they are essential for the success of LLMs today.

What are the basics of LLM parameters?

In LLMs, parameters refer to the values that guide the model's decisions. Weights adjust the importance of different pieces of data, while biases shift the model's output in a given direction. Together, they help the model analyze information and predict outcomes more accurately by fine-tuning these values during training. As the model processes more data, it learns to adjust weights and biases to improve its predictions.

LLM parameter examples

LLMs rely on various parameters and hyperparameters, such as weights, biases, temperature, and activation functions, to function correctly. Each component is crucial in the model’s data processing and response generation capabilities. By carefully adjusting these parameters, you can build more effective models tailored to specific tasks. The following parameters can significantly impact model performance:

• Weights: This element controls how the importance the model assigns to different inputs—the larger the assigned weight, the more influential that input is to the outcome.

• Biases: Biases are values added to inputs from the previous layer and are used to fine-tune predictions, helping the model adjust and make sense of input data.

• Hyperparameters: These variables, set beforehand, manage the training process by defining how the model learns. For example, the learning rate determines how fast the model adjusts its parameters, and batch size sets how much data the model processes before updating its parameters during training.

• Temperature: This feature changes how creative the model’s responses are. Higher temperature values allow models to produce more varied and unexpected answers, while lower values result in safer, more predictable responses.

• Activation functions: Activation functions use math to determine the extent of neuron activation based on input. They help the model understand complex patterns by keeping values within a manageable range, preventing them from becoming too large or too small. It helps with performing various tasks, including understanding natural language, time-series analysis, and predicting trends.

Fine-tuning weights, biases, and hyperparameters improves model performance. Neural network optimization goes beyond this, adjusting the model’s architecture and training techniques to enhance efficiency and accuracy.

How parameters affect LLM performance

LLM parameters directly influence the model’s performance. You can adjust the parameters to improve how well the model learns and handles new data. Understanding how model size and the parameter-tuning process may affect model training by preventing overfitting, underfitting, excessive model bias or variance, and generalization can help enhance model performance.

Choosing the appropriate model size

The number of parameters directly affects the model's capacity. Larger model sizes and an increased number of parameters help capture more complex patterns. However, this also necessitates using more data and computing power to train effectively.

Preventing overfitting in machine learning

When a model learns the training data too well and can’t generalize to new data, it causes overfitting. In this instance, the model’s performance is positive when dealing with training data, but it struggles to perform well with new data. Techniques like early stopping, pruning, regularization, ensembling, data augmentation, and training with more data can help avoid overfitting.

Avoiding underfitting for better model performance

Underfitting occurs when a meaningful relationship between the input and output data can’t be determined in the training set. This might happen because the model had insufficient training time on an appropriate number of data points. An underfit model struggles to identify the main trends in the data and experiences high bias. 

To mitigate underfitting, ensure the model is sufficiently complex to capture key patterns in the data, reduce regularization, extend the training time, and add more relevant features. 

Improving generalization in LLMs

Generalization is an LLM’s ability to apply what it learned from training data to new data. Parameters enable LLMs to learn patterns from training data and adjust to new inputs. Fine-tuning adjusts these parameters, improving performance on specific tasks with minimal data.

Who uses LLM parameters?

LLMs are quickly becoming indispensable tools in industries such as health care, law, and finance, among others. Professionals across various fields benefit from LLMs with parameters finely tuned to meet their needs. These models offer increased efficiency, improved customer satisfaction, data-driven decision-making, and cost savings, transforming the way businesses function and engage with their customers. 

Explore examples of professionals who commonly use LLM parameters, along with the average base salary of each position.

1. Data scientists

Average annual base salary: $119,655 [1]

Data scientists use LLMs to analyze data and build models to derive insights and make predictions. As LLMs' capabilities increase, data scientists are transitioning from hands-on coding and standard data analysis to assessing and managing analyses automated by LLMs.

2. Machine learning engineers

Average annual base salary: $124,295 [2]

Machine learning engineers work with large data sets, preprocessing and extracting features from the data. As a machine learning engineer, your responsibilities will likely include collecting and preparing data that best meets the needs of the businesses you are working with. After your data is prepared, you might train, test, fine-tune, and optimize models to improve accuracy.

3. AI research scientist

Average annual base salary: $100,379 [3]

AI research scientists focus on innovation. In this position, you might refine theoretical concepts, create new algorithms, or improve upon existing ones. A significant part of your work will likely involve academic and experimental research. Knowledge of machine learning techniques and expertise in deep learning, including familiarity with LLM parameters, are vital in the role of an AI research scientist.

4. AI engineer

Average annual salary: $135,089 [4]

AI engineers transform AI research scientists' theories, algorithms, and discoveries into actionable solutions. This is a hands-on role in which you will likely employ coding, testing, and debugging skills to ensure scalable and efficient AI solutions. Knowledge of deep learning platforms, including familiarity with LLM parameters, is essential for an AI engineer.

Best practices for working with LLM parameters

When working with LLM parameters, following effective strategies, such as regularization, hyperparameter tuning, and performance monitoring, to enhance model performance and promote generalization is essential. Discover best practices for managing and optimizing these parameters:

• Regularization techniques: To prevent overfitting, regularization techniques help the model focus on underlying patterns rather than memorization. Dropout is a regularization technique in which some neurons are randomly selected to output to the next layer, and others are ignored, updating weights and encouraging more generalized learning. A second form of regularization, weight decay, adds a penalty to large weights, promoting simpler models that generalize better.

• Hyperparameter tuning: This technique optimizes a model's efficiency, performance, and computational resource use. Grid search algorithms test various combinations of parameters to find the best settings. Using previous results, Bayesian optimization guides the search for optimal parameters, making the process more efficient for complex models.

• Monitoring performance: Regularly assessing a model’s performance ensures parameter changes lead to real improvements. Validation data sets help track how well the adjustments improve the model’s ability to generalize its learnings to new, unseen data.

How to learn about LLM parameters

A variety of options, including online courses, documentation and tutorials, and research materials, exist to expand your understanding of LLM parameters. A deeper understanding of this topic can help you see how models process information, make predictions, and improve performance.

Take online courses.

Online platforms offer courses that explain how LLM parameters shape model behavior. For example, Coursera offers Specializations and Professional Certificates in machine learning and deep learning. These courses provide training on practical skills and knowledge that machine learning experts use in their daily roles. 

Study documentation and tutorials.

LLM developers publish detailed documentation explaining how each parameter influences model performance. Studying these materials can help learners understand how to configure models for different applications. Community-driven support around open-source LLMs flourishes through forums and mailing lists where users can collaborate to solve challenges. This collective effort can guide users through adjusting parameters, fine-tuning, and troubleshooting.

Read research papers.

Researchers continuously refine LLM architectures and optimization strategies. Reading papers from sources like arXiv and Google Research can help learners understand how experts adjust parameters to improve accuracy, efficiency, and fairness.

Learn more about machine learning with Coursera

LLM parameters enable large language models to learn and evolve into valuable tools. Understanding how these parameters guide ongoing learning and influence performance is crucial in today’s rapidly advancing AI landscape.

Learn more about applying best practices for machine learning development and using unsupervised learning techniques for unsupervised learning, including clustering and anomaly detection, with the Machine Learning Specialization from Stanford University and DeepLearning.AI on Coursera.

For more advanced learning, explore machine learning, deep learning, neural networks, and machine learning algorithms like classification, regression, clustering, and dimensional reduction. You can build deep learning models and neural networks in the IBM AI Engineering Professional Certificate program, also on Coursera.