What is a large language model (LLM)?

A large language model (LLM) is an advanced AI program designed to recognize and generate text among other capabilities. The terminology “large” is used to refer to these models since they are trained on extensive datasets. Built using machine learning, particularly a neural network type known as a transformer model, LLMs can interpret and understand human language by being trained on vast amounts of data, often sourced from the internet. However, the quality of the data is crucial for effective learning, so developers may opt for more curated datasets. LLMs employ deep learning, a subset of machine learning, to analyze unstructured data probabilistically. This process enables the model to distinguish between different content elements independently. To optimize performance for specific tasks, LLMs undergo further training through fine-tuning or prompt-tuning, enabling them to perform functions such as answering questions or translating languages accurately.

 

What are Large Language Models (LLMs) used for?

Large language models (LLMs) are versatile tools trained for various tasks. One of their most popular uses is in generative AI, where they produce text in response to prompts or questions. For example, the widely-known LLM, ChatGPT, can create essays, poems, and other written forms based on user inputs.

LLMs can be trained on extensive and complex datasets, including programming languages, enabling them to assist programmers in writing code. They can generate functions on demand or complete partially written programs. LLMs are also employed in sentiment analysis, DNA research, customer service, chatbots, and online searches.

Examples of LLMs in real-world applications include ChatGPT by OpenAI, Google’s Bard, Meta’s Llama, and Microsoft’s Copilot. GitHub’s Copilot specifically assists with coding, showcasing the diverse applications of LLMs beyond natural human language.

What are some advantages and limitations of LLMs?

Large language models (LLMs) have the notable advantage of handling unpredictable queries. Unlike traditional computer programs, which operate within a defined set of inputs or commands—such as specific buttons in a video game or precise if/then statements in a programming language—LLMs can understand and respond to natural human language. They use data analysis to answer unstructured questions in a meaningful way. For example, while a typical computer program might not comprehend a question like “What are the four greatest rock bands in history?”, an LLM could generate a list of bands and provide a coherent rationale for the choices.

However, the reliability of LLMs is dependent on the quality of the data they are trained on. If they are fed incorrect information, they will produce incorrect responses. Additionally, LLMs can sometimes “hallucinate,” meaning they generate false information when they cannot produce an accurate answer. For instance, in 2022, when Fast Company asked ChatGPT about Tesla’s financial performance in the previous quarter, ChatGPT produced a plausible news article, but much of the information was fabricated.

In terms of security, applications using LLMs are just as susceptible to bugs as any other software. Additionally, LLMs can be influenced by malicious inputs to generate specific types of responses, including those that may be harmful or unethical. Another security concern is that users might upload confidential data into LLMs to boost their productivity. Since LLMs use the data they receive for further training and are not designed as secure storage, they could potentially disclose sensitive information in responses to other users’ queries.

How do Large Language Models (LLMs) work?

At their core, LLMs are based on machine learning, a branch of AI that involves training a program with large datasets so it can identify features within the data autonomously. Specifically, LLMs utilize deep learning, a form of machine learning where models essentially train themselves to recognize patterns and distinctions without direct human guidance, although some human fine-tuning is usually involved.

Deep learning relies on probability to “learn.” For example, in the sentence “The quick brown fox jumped over the lazy dog,” the letters “e” and “o” appear four times each, making them common. A deep learning model would infer that these characters are highly likely to appear in English text. While a single sentence is insufficient for the model to draw significant conclusions, analyzing trillions of sentences enables the model to predict how to logically complete incomplete sentences or generate new sentences.

Neural Networks

LLMs leverage neural networks to facilitate deep learning. Similar to how the human brain consists of interconnected neurons that transmit signals, artificial neural networks (often referred to as “neural networks”) are made up of nodes that interconnect. These networks include several layers: an input layer, an output layer, and one or more intermediate layers. Information is only passed between layers if the nodes’ outputs exceed a certain threshold.

Transformer Models

The neural networks used in LLMs are specifically transformer models. These models are particularly adept at learning context, which is crucial for understanding human language. Transformer models utilize a mathematical technique known as self-attention to identify subtle relationships between elements in a sequence. This capability makes them superior to other machine learning types in grasping context, such as the connection between the end and the beginning of a sentence or the relationship between sentences in a paragraph.

This allows LLMs to interpret human language even when it is vague, poorly defined, or presented in novel combinations and contexts. Transformer models can “understand” semantics to a certain degree by associating words and concepts based on their meanings, having observed these groupings millions or billions of times.

What are Small Language Models (SLMs) and how do they differ from Large Language Models (LLMs)?

Small language models (SLMs) are efficient neural networks designed for language tasks, operating with fewer resources and less computing power compared to larger models. They function almost as effectively as larger language models but with reduced computational demands.

Consider a language model as a student learning a new language. An SLM is akin to a student with a smaller notebook to jot down vocabulary and grammar rules. While they can still learn and use the language, they might struggle to remember as many complex concepts or nuances as a student with a larger notebook (a larger language model).

The primary advantage of SLMs is their speed and reduced computational requirements, making them ideal for resource-limited applications like mobile devices or real-time systems. However, the downside is that SLMs may not perform as well on more complex language tasks, such as understanding context, answering intricate questions, or generating highly coherent and nuanced text.

Both SLMs and LLMs are based on similar probabilistic machine learning principles for their architecture, training, data generation, and model evaluation. However, they differ in their scale, resource requirements, and performance capabilities.

Size and Model Complexity

The most noticeable distinction between SLMs and LLMs lies in their size. For instance, LLMs like ChatGPT (GPT-4) reportedly contain around 1.76 trillion parameters, while open-source SLMs such as Mistral 7B have about 7 billion parameters. This difference stems from their training processes and model architectures. ChatGPT employs a self-attention mechanism within an encoder-decoder model framework, whereas Mistral 7B utilizes sliding window attention for efficient training in a decoder-only model.

Contextual Understanding and Domain Specificity

SLMs are typically trained on data from specific domains, meaning they may lack comprehensive contextual information across multiple knowledge areas but excel within their specialized domain. In contrast, the goal of an LLM is to mimic human intelligence on a broader scale. LLMs are trained on extensive datasets and are designed to perform well across a wide range of domains, making them more versatile. This versatility allows LLMs to be adapted, improved, and engineered for various downstream tasks, such as programming, more effectively than domain-specific SLMs.

Resource Consumption

Training an LLM is a computationally intensive task that necessitates GPU resources at scale in cloud environments. Training ChatGPT from scratch, for example, requires thousands of GPUs, whereas running Mistral 7B, an SLM, can be managed on local machines with a decent GPU, although training a model with 7 billion parameters still demands several computing hours across multiple GPUs.

Bias

LLMs are often biased due to inadequate fine-tuning and training on publicly accessible raw data sourced from the internet. This training data can:

• Underrepresent or misrepresent certain groups or ideas.
• Be labeled incorrectly.

Additionally, inherent biases in language itself, influenced by factors like dialect, geographic location, and grammar rules, can further complicate biases within LLMs. Furthermore, the model architecture itself can unintentionally reinforce biases, which may go unnoticed during development.

Since SLMs train on smaller, domain-specific datasets, they generally pose a lower risk of bias compared to LLMs.

Inference Speed

The smaller size of SLMs allows users to run the model on local machines while still achieving acceptable inference speeds. In contrast, LLMs require multiple parallel processing units to handle inference. Depending on the number of concurrent users accessing an LLM, inference speed can decrease significantly.

What is the difference between large language models and generative AI?

Large language models and generative AI are distinct but overlapping domains within artificial intelligence. Generative AI serves as a broad category encompassing various AI models capable of producing content across different mediums, including text, code, images, video, and music. ChatGPT, Midjourney and DALL-E are all examples of generative AI.

Specifically, large language models are a subset of generative AI specifically designed and trained to generate textual content. ChatGPT, for instance, exemplifies a prominent large language model focused on generating text-based outputs.

It’s important to note that while all large language models fall under the umbrella of generative AI, not all generative AI models are specifically large language models trained on text.

Future Advancements in Large Language Models (LLMs)

Future advancements in large language models (LLMs) are poised to yield substantial enhancements in accuracy and comprehension, enabling more precise and contextually aware responses. These advancements are expected to incorporate multimodal capabilities, integrating text, images, videos, and audio processing to facilitate more comprehensive interactions. Personalization features will be bolstered, allowing LLMs to tailor responses based on individual user preferences and historical interactions.

Efforts to optimize efficiency and speed will reduce the computational resources required for both training and inference tasks. There will also be a concerted focus on mitigating biases and ensuring ethical usage, alongside enhancements in real-time learning capabilities to adapt swiftly to new data inputs. Domain-specific LLMs will emerge, offering specialized expertise and delivering more pertinent responses across various fields.

Collaboration and integration with other AI systems will be enhanced, enabling the development of sophisticated and cohesive solutions. Improvements in interactivity and conversational AI will foster more natural and seamless multi-turn conversations, enhancing the human-like interaction experience. Moreover, advancements aimed at reducing costs and lowering technical barriers are expected to expand the accessibility and scalability of LLM technology for developers and businesses of diverse scales.

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