# Large Language Models

Large Language Models (LLMs) are Transformer-based systems trained on vast text to generate and understand language. This sub-article expands [Section 1 of Recent Topics](../recent.md) and [Transformers](transformers.md).

## Capabilities
- Text generation, summarization, translation, code, reasoning, and few-shot learning.
- **Scaling laws**: performance improves predictably with compute, data, and parameters.

## Frontier Models
- GPT-4, Claude, Gemini, Llama, Mistral, and others lead benchmarks.
- **Open vs. closed** debate: openness aids research but raises misuse risk.

## Benchmarks and Evaluation
- MMLU, HumanEval, ARC, and new evals track capability.
- Leaders saturate old tests, prompting harder, more robust ones.

## Limitations
- Hallucination, brittleness, and sensitivity to prompt wording.
- Context limits and high inference cost constrain use.

## Why It Matters
LLMs are the current workhorse of general-purpose AI, shaping agents, multimodal systems, and public debate.

## Sources
- OpenAI. *GPT-4 Technical Report* (2023).
- Kaplan, J. et al. (2020). *Scaling Laws for Neural Language Models.*

*Back to [Recent Topics](../recent.md).*