Large Language Models
Large Language Models (LLMs) are Transformer‑based models trained on massive text corpora (billions of tokens). They exhibit emergent abilities like few‑shot learning, reasoning, and code generation. Examples include GPT‑4, Llama, Claude, Gemini.
LLMs = large‑scale transformers trained with self‑supervised objectives (next token prediction, masked language modeling).
Scaling Laws
Performance improves predictably with model size, dataset size, and compute. This led to the "bitter lesson": more data and compute often beat algorithmic innovation.
Training Stages
- Pre‑training: next‑token prediction on huge text (unsupervised).
- Fine‑tuning: supervised fine‑tuning (SFT) on instruction‑response pairs.
- RLHF: reinforcement learning from human feedback to align with human preferences.
Emergent Abilities
Beyond a certain scale, LLMs show abilities not present in smaller models: in‑context learning (few‑shot), chain‑of‑thought reasoning, translation, summarization, code generation.
Notable LLMs
- GPT‑3.5 / GPT‑4 (OpenAI): general purpose, ChatGPT.
- Llama (Meta): open weights, widely used for fine‑tuning.
- Claude (Anthropic): safety‑focused.
- Gemini (Google): multimodal (text, image, audio).
- Mistral: efficient, open.
Two Minute Drill
- LLMs are large Transformers trained on massive text.
- Emergent abilities: few‑shot learning, reasoning, code generation.
- Training: pre‑training → fine‑tuning → RLHF.
- Examples: GPT‑4, Llama, Claude, Gemini.
Need more clarification?
Drop us an email at career@quipoinfotech.com