Large Language Model (LLM) – Definition & Explanation

What is a Large Language Model (LLM)?

A Large Language Model (LLM) is an artificial neural network trained on massive amounts of text data to understand and generate human language. LLMs like GPT-4, Claude, Gemini, and LLaMA can write texts, answer questions, write code, and solve complex tasks.

The term "Large" refers to the number of parameters – modern LLMs have hundreds of billions of parameters that are optimized during training. The more parameters, the more complex patterns the model can capture.

How Do LLMs Work?

LLMs are based on the Transformer architecture, introduced by Google in 2017. The core is the "attention mechanism," which allows the model to recognize relevant relationships in text – even across large distances.

Training in Three Phases

1. Pre-Training: The model learns from billions of texts (books, websites, Wikipedia) to predict the next words. This develops a deep understanding of language.
2. Fine-Tuning: The model is adapted to specific tasks or formats, such as following instructions or answering questions in a dialogue format.
3. RLHF (Reinforcement Learning from Human Feedback): Humans rate the model's responses, and it learns to prioritize helpful, harmless, and honest answers.

Popular LLMs Overview

GPT-4 / GPT-4o (OpenAI)

The GPT series (Generative Pre-trained Transformer) from OpenAI is the most well-known LLM. ChatGPT is based on these models. GPT-4 supports multimodal inputs (text and image) and has a context window of up to 128,000 tokens.

Claude (Anthropic)

Claude is known for particularly long context windows (up to 200,000 tokens) and a focus on safety through "Constitutional AI." The current version Claude 3.5 Sonnet is considered one of the most capable models on the market.

Gemini (Google)

Google's LLM family ranges from Gemini Nano for mobile devices to Gemini Ultra for complex reasoning tasks. The models are natively multimodal and can process text, image, audio, and video.

LLaMA / Llama (Meta)

Meta's open-source LLMs have revolutionized the developer community. Llama 3 is freely available and forms the foundation for many specialized models.

Applications of LLMs

• Text Generation: Blog posts, emails, marketing copy
• Programming: Code generation, debugging, code reviews
• Customer Service: Chatbots and automated responses
• Translation: High-quality translations into dozens of languages
• Research: Summarizing documents and extracting facts
• Education: Personalized tutoring and explanations

Limitations and Challenges

Hallucinations

LLMs can generate convincing-sounding but factually incorrect information. They sometimes "invent" facts, quotes, or sources. Therefore, critical review of outputs is important.

Knowledge Cutoff

LLMs have a knowledge cutoff date – they only know information up to a certain point in time. Current events are unknown to them unless they have access to external tools like web search.

Context Window Limitation

Although modern LLMs have large context windows, the amount of text they can process simultaneously is limited. With very long documents, the quality of responses may decrease.

Bias and Fairness

LLMs reflect the biases in their training data. Despite intensive efforts toward fairness, they can reproduce stereotypical or discriminatory patterns.

Using LLMs Effectively

To get the most out of LLMs, good prompts are crucial. Techniques like Chain-of-Thought Prompting can significantly improve the quality of responses.

For developers, APIs from OpenAI, Anthropic, and Google offer the ability to integrate LLMs into their own applications. Costs are typically calculated based on tokens consumed.

Comprehensive LLM Parameter List

The following interactive table shows over 60 well-known Large Language Models with their parameter counts. You can search by name, filter by developer, size category or model type, and sort the columns:

Conclusion

Large Language Models have fundamentally changed how we interact with computers. They are powerful tools for text processing, programming, and creative tasks – but not a replacement for human judgment and expertise. Those who understand their strengths and limitations can effectively use them for a variety of tasks.