What Is a Large Language Model (LLM)?
A Large Language Model is a specialized branch of Natural Language Processing (NLP) that creates computer programs designed to imitate human intelligence. These models are characterized by four technical pillars: neural networks, tokens, context windows, and scaling.
Neural Networks
The structural foundation of an LLM is the artificial neural network, a computational model composed of mathematical functions that compute logical operations. These networks use a system of artificial neurons to represent and process information via weights and optimization targets. At the University of Central Florida, research into these networks extends to the Department of Physics, where faculty apply quantum computing methods to improve how neural networks prepare and represent data, which can increase classification accuracy while reducing computational complexity.
Tokens
Large language models do not process language word-by-word; instead, they utilize units called “tokens”. A token is a basic unit of meaning, typically representing a word fragment or a common character sequence. The system converts these fragments into numbers that the artificial neural network can process mathematically. A model’s speed, memory capacity, and processing costs are generally calculated based on these tokens rather than word counts.
Concepts such as tokenization and language representation are explored in programs like computer science majors, where students study natural language processing and deep learning systems.
Context Window
The context window refers to the model’s “memory” during a specific interaction session. Because models are designed to be context-aware, they can reference previous parts of a conversation to answer follow-up questions. However, this memory is finite. If a conversation becomes too long or includes too many unrelated topics, the model may begin to yield inconsistent or unhelpful outputs. Users must start a “new chat” to clear the context window and provide a blank slate for the model’s next prediction cycle.
Scaling
Scaling refers to increasing the amount of training data and computational power to improve a model’s performance. Modern LLMs require massive clusters of Graphics Processing Units (GPUs) and significant amounts of electricity and water to function. UCF Professor Jun Wang, researches these scaling challenges, specifically investigating informational bottlenecks that cause AI models to move data they do not need, wasting time and energy.
What Is a Transformer Model?
Modern generative AI systems rely on an architectural advancement known as the transformer. Proposed in 2017, the transformer architecture allows models to learn linguistic patterns more effectively than previous software designs by utilizing attention mechanisms that help the model determine which parts of the input are most relevant.
Attention
Attention is the mechanism that enables a model to focus on specific, relevant parts of an input sequence to determine context and meaning. Instead of treating all input data as equally important, the model assigns different weights to different tokens. UCF Assistant Professor Jaeyoung Park utilizes self-attention mechanisms to help models understand the context of repeated measurements in physical activity data. Similarly, UCF researchers studying intrinsically disordered proteins utilize attention modules to focus on specific physical features that impact biological behavior.
Parallel Processing
Previous language models often processed data sequentially, which was limited by hardware speed. Transformers enable parallel processing, allowing the system to perform multiple calculations simultaneously. This requires precise coordination between the software algorithm and hardware components, like GPUs and operating systems. Professor Wang’s research focuses on improving this coordination to reduce the “start times” and communication delays between these internal components.
Long-Range Context
The transformer architecture excels at maintaining long-range dependencies, or the ability to connect information from the beginning of a long sequence to make sense of the end. This is essential for consistency in generating long-form text or analyzing complex data. UCF Assistant Professor Yu Tian applies this architectural capability to medical world models, enabling AI to analyze a patient’s historical data to project future health trajectories up to five years in advance.
Transformer architectures and large-scale model training are examined in graduate programs such as the Computer Science MS, Big Data Analytics Ph.D., and Statistics and Data Science MS, where students analyze distributed systems, optimization, and model evaluation methods.
How Does the Model Generate a Response?
Generating a response follows a structured mathematical process involving tokenization, probability distributions, and an iterative loop.
Tokenization
When a user enters a prompt—a request or question written in natural language—the system first converts the text into machine-usable numbers. This tokenization process breaks sentences into tokens that the artificial neural network can process through mathematical functions.
Probability Distribution
Once the input is tokenized, the model calculates a probability distribution for the next likely token. It does not access a database of answers; it uses the learned weights to determine which word fragment is most likely to follow the sequence it has generated so far. UCF Assistant Professor Shashank Sonkar conducts research into model calibration, investigating whether an LLM’s self-reported confidence in its prediction matches its actual accuracy.
Iterative Next-Token Loop
The generation is an iterative process. After the model predicts the first token, that token is added back to the input sequence, and the calculation begins again for the next token. This cycle repeats word by word or sentence by sentence until a complete response is formed. This sequential nature is why tools like ChatGPT appear to “type” their responses in real-time.
Language models at the very basic level just predict the next token. They don’t care about what is true.”
How Are AI Models Trained?
LLMs undergo a multi-stage training process to transition from raw data to functional conversational tools.
Pretraining
The initial phase, pretraining or “ingestion,” involves exposing the algorithm to massive datasets. These datasets contain billions or trillions of pages of information, including academic articles, books, and digital forums such as Reddit. During this phase, the model learns common linguistic forms and the abstract patterns of human language.
Fine-Tuning
After pretraining, models undergo “fine-tuning” using smaller, specialized datasets to adapt them for specific tasks. Dr. Sonkar focuses on pedagogical fine-tuning to develop AI tutors that can lead students through sub-problems rather than simply providing immediate answers. Because full fine-tuning of billions of parameters is computationally expensive, UCF Assistant Professor Aritra Dutta researches Parameter-Efficient Fine-Tuning (PEFT). Professor Dutta’s work focuses on Low-Rank Adaptation (LoRa), which updates only a small subset of parameters to reduce memory requirements while maintaining performance.
Reinforcement Learning from Human Feedback
To ensure outputs align with human values and safety requirements, developers implement guardrails and moderation systems. This alignment process helps mitigate early model flaws, such as cultural bias or aggressive behavior learned from certain online training sources. Computer science lecturer Jie Lin focuses on adaptive AI under security constraints, developing defense techniques to mitigate “harmful fine-tuning” attacks that aim to bypass these safety constraints.
Why Does AI Hallucinate?
Hallucinations are a structural byproduct of how Large Language Models are designed.
“A hallucination is where [the LLM] has simply predicted the wrong thing, or actually in some cases invented something that isn’t even correct.”
Statistical Completion vs. Verification
The primary cause of hallucinations is that LLMs are built for statistical completion rather than factual verification. The model optimizes for the “plausible next word” rather than checking its output against a database of truths. Consequently, an LLM can invent names, quotes, and academic citations that appear professional but have no basis in reality. This “truthiness” makes it impossible to distinguish accurate from inaccurate information solely by the tone of the output.
Structural Limits
Hallucinations can also increase when models are trained on data that includes AI-generated content, which can reinforce existing errors over time. This can amplify existing errors and biases over time. Additionally, the “black box” nature of these models means that even when a model produces a correct answer, the internal reasoning process that led to that result is often too complex for developers to interpret directly. Assistant Professor Song Wang specializes in trustworthy AI, focusing on the transparency of these models to ensure their internal reasoning steps are faithful to the final results.
Evaluating and Advancing Large Language Models
Generative AI research at UCF contributes to both the development and evaluation of large-scale neural networks.
In medical imaging, Associate Professor of Medicine Laura Brattain applies diffusion architectures to generate synthetic ultrasound images that support the training of diagnostic models.
Tony Magliocco, volunteer faculty in pathology at UCF’s College of Medicine, applies machine learning techniques to identify gene patterns associated with therapy resistance, demonstrating how structured training data informs predictive modeling.
Researchers have also developed foundational datasets for training and evaluating neural networks. The UCF101 dataset, created by Trustee Chair Professor Mubarak Shah and his team at UCF’s Center for Research in Computer Vision, contains 13,320 labeled video clips across 101 action categories and remains a widely used benchmark in deep learning research—earning its creators the PAMI Mark Everingham Prize for its impact on computer vision. Datasets such as UCF101 illustrate how neural networks learn from structured examples to identify patterns across complex inputs.
In addition, Associate Professor Chen Chen studies federated learning. This distributed training method allows AI systems to learn from data stored across separate institutions without sharing raw information. This approach supports privacy-preserving model training while expanding dataset ranges.
Tools like ChatGPT generate language by calculating probabilities across token sequences within a transformer-based neural network. They do not retrieve stored facts or reason in a human sense; they estimate statistically likely continuations based on patterns learned during training. Research at UCF continues to examine how architectural design, scaling strategies, calibration methods, and evaluation frameworks influence reliability, efficiency, and interpretability.
Understanding these mechanisms provides a foundation for evaluating how generative systems function within the technical constraints of their design and training data.
Summary: How Tools Like ChatGPT Work
- Generative AI systems function as probabilistic language models that produce text by estimating conditional probabilities within the structural limits of their architectures and training data.
- Large language models operate using artificial neural networks, mathematical systems designed to identify patterns in data. Most modern systems rely on the transformer architecture, which uses attention mechanisms to evaluate relationships between words and determine context across large training datasets.
- The model processes language by breaking text into tokens—small units such as word fragments—and generates responses through next-token prediction. Rather than retrieving stored facts, it estimates the statistically probable continuation of a sequence based on patterns learned during training.
- A primary limitation of this approach is the potential for hallucinations, where the system produces inaccurate information or fabricated citations with high confidence. These errors occur because the model predicts likely language patterns rather than verifying factual correctness.
- To improve reliability, Assistant Professor Shashank Sonka studies model calibration, developing methods to better align system confidence with measured predictive accuracy.
- Scaling large language models requires significant computational resources. UCF faculty including, Professor Wang, investigate efficiency techniques such as Parameter-Efficient Fine-Tuning and Low-Rank Adaptation, which adapt models for specialized tasks while updating only a subset of internal parameters.
- These generative architectures are also applied to discipline-specific research, including medical modeling and synthetic imaging led by Assistant Professor Tian and Associate Professor Brattain.
Frequently Asked Questions About How ChatGPT Works
ChatGPT uses a process called next-token prediction. It analyzes a user’s prompt and calculates the statistically most probable next word or fragment based on patterns in its training data. This cycle repeats sequentially until a complete response is formed
AI hallucinations occur when a model produces fabricated or incorrect information while maintaining a confident tone. Because the system is designed for statistical completion rather than factual verification, it may generate plausible-sounding but inaccurate content.
GPT stands for Generative Pre-trained Transformer. “Generative” means the system can create new text. “Pre-trained” means it learned language patterns from large datasets before being used in applications like ChatGPT. “Transformer” refers to the type of neural network architecture that allows the model to analyze context and relationships between words.
A large language model is a type of generative artificial intelligence trained on large datasets to predict the next word or phrase in context. It generates text by estimating statistical probabilities learned during training. LLMs do not access a real-time database of facts; they rely on patterns established during model training.
Tokenization is the process of breaking language into smaller units called tokens, often fragments of words. Large language models use tokens as the primary units for processing input and generating output. Model memory limits and processing costs are typically measured in tokens rather than words.