About Large Language Models (LLMs):
- An LLM is a type of artificial intelligence (AI) program that can recognize and generate text, among other tasks.
- LLMs are trained on huge sets of data, hence the name “large.”
- LLMs are built on machine learning: specifically, a type of neural network called a transformer model, which excels at handling sequences of words and capturing patterns in text.
- In simpler terms, an LLM is a computer program that has been fed enough examples to be able to recognize and interpret human language or other types of complex data.
- Many LLMs are trained on data that has been gathered from the Internet—thousands or millions of gigabytes’ worth of text.
- But the quality of the samples impacts how well LLMs will learn natural language, so LLM's programmers may use a more curated data set.
- LLMs use a type of machine learning called deep learning in order to understand how characters, words, and sentences function together.
- Deep learning involves the probabilistic analysis of unstructured data, which eventually enables the deep learning model to recognize distinctions between pieces of content without human intervention.
- LLMs are then further trained via tuning: they are fine-tuned or prompt-tuned to the particular task that the programmer wants them to do.
- What are LLMs Used For?
- LLMs can perform various language tasks, such as answering questions, summarizing text, translating between languages, and writing content.
- Businesses use LLM-based applications to help improve employee productivity and efficiency, provide personalized recommendations to customers, and accelerate ideation, innovation, and product development.
- LLMs serve as the foundational powerhouses behind some of today’s most used text-focused generative AI (GenAI) tools, such as ChatGPT, Claude, Microsoft Copilot, Gemini, and Meta AI.
- Since LLMs are now becoming multimodal (working with media types beyond text), they are now also called “foundation models”.
- Though they are groundbreaking, LLMs face challenges that may include computational requirements, ethical concerns, and limitations in understanding context.
Quick Definitions:
- Machine learning: A subset of AI where data is fed into a program so it can identify features in that data.
- Deep learning: Trains itself to recognize patterns without human intervention.
- Neural networks: Constructed of connected network nodes composed of several layers that pass information between each other.
- Transformer models: Learn context using a technique called self-attention to detect how elements in a sequence are related.
