← Back to Portal
Large Language Models
Explore the revolutionary world of Large Language Models - from transformer architecture to modern applications
🏗️ Transformer Architecture
The foundation of modern LLMs. Transformers use self-attention mechanisms to process sequences in parallel,
enabling better context understanding and faster training compared to recurrent architectures.
🔄 Attention Mechanisms
Self-attention allows models to weigh the importance of different words in a sequence,
creating rich contextual representations that capture long-range dependencies and relationships.
📊 Scaling Laws
Empirical relationships showing how model performance improves with increased parameters, data, and compute.
These laws guide the development of increasingly powerful language models.
🎯 Pre-training & Fine-tuning
LLMs are first pre-trained on massive text corpora to learn language patterns,
then fine-tuned on specific tasks to achieve specialized performance.
Evolution of Language Models
2017
Transformer Architecture
"Attention is All You Need" paper introduces the transformer architecture,
revolutionizing NLP with self-attention mechanisms and parallel processing.
2018
BERT & GPT-1
BERT introduces bidirectional training while GPT-1 demonstrates the power of
autoregressive language modeling with 117M parameters.
2019
GPT-2
GPT-2 with 1.5B parameters shows emergent capabilities and initially withheld
due to concerns about potential misuse.
2020
GPT-3
GPT-3's 175B parameters demonstrate few-shot learning capabilities,
marking the beginning of the modern LLM era.
2022
ChatGPT & InstructGPT
Introduction of RLHF (Reinforcement Learning from Human Feedback) creates
more helpful, harmless, and honest AI assistants.
2023-2024
GPT-4 & Multimodal LLMs
Advanced reasoning capabilities and multimodal understanding,
processing both text and images with unprecedented accuracy.
Popular Language Models
GPT-4
OpenAI
Most advanced language model with multimodal capabilities, excellent reasoning,
and strong performance across diverse tasks.
Type:
Autoregressive
Modality:
Text + Images
Context:
128K tokens
Claude 3.5 Sonnet
Anthropic
Advanced AI assistant focused on being helpful, harmless, and honest,
with strong analytical and creative capabilities.
Type:
Constitutional AI
Context:
200K tokens
Focus:
Safety & Reasoning
Gemini Ultra
Google
Multimodal AI model designed to understand and generate text, code, audio,
image, and video content with state-of-the-art performance.
Type:
Multimodal
Capabilities:
Text, Code, Audio, Vision
Integration:
Google Services
Llama 2
Meta
Open-source foundation model available in multiple sizes,
enabling research and commercial applications with responsible AI practices.
Type:
Open Source
Sizes:
7B, 13B, 70B
License:
Custom Commercial
Key Concepts
🎯 Emergent Abilities
Capabilities that appear in large models but not smaller ones, such as
few-shot learning, chain-of-thought reasoning, and complex instruction following.
🔧 Prompt Engineering
The art and science of crafting effective prompts to guide LLM behavior,
including techniques like few-shot examples and chain-of-thought prompting.
🎖️ RLHF
Reinforcement Learning from Human Feedback aligns model outputs with human preferences,
improving helpfulness while reducing harmful or biased responses.
🏗️ In-Context Learning
The ability to learn new tasks from examples provided in the input prompt,
without updating model parameters through traditional training.
🔍 Hallucination
When models generate plausible-sounding but factually incorrect information,
a key challenge in deploying LLMs for factual applications.
⚖️ Alignment
Ensuring AI systems behave in ways that are beneficial, safe, and aligned
with human values and intentions.