Large Language Models (LLMs) are classified based on their architecture, purpose, training approach, scale, and deployment. Here’s a breakdown of the different types of LLM models:
1. Based on Model Architecture
- Transformer-Based Models
Use the transformer architecture for natural language understanding and generation.
Examples: GPT, BERT, T5, RoBERTa. - RNN-Based Models
Use recurrent neural networks or LSTMs (Long Short-Term Memory) to process sequential data.
Examples: Early Seq2Seq models. - Hybrid Models
Combine transformers with other techniques like memory augmentation or retrieval modules.
Examples: RETRO, RAG (Retrieval-Augmented Generation).
2. Based on Training Approach
- Autoregressive Models
Generate text by predicting the next word/token based on previous ones.
Examples: GPT, XLNet. - Masked Language Models (MLMs)
Predict masked (hidden) words within text, often used for fine-tuning.
Examples: BERT, RoBERTa. - Sequence-to-Sequence (Seq2Seq) Models
Map input sequences to output sequences, ideal for tasks like translation.
Examples: T5, mT5, BART.
3. Based on Functionality
- General-Purpose Models
Versatile models for various tasks, from chatbots to summarization.
Examples: GPT-4, PaLM, Claude. - Domain-Specific Models
Fine-tuned for specialized fields like healthcare or finance.
Examples: BioBERT, FinBERT. - Multimodal Models
Handle multiple data types (e.g., text, images, audio).
Examples: GPT-4 Vision, DALL-E, CLIP.
4. Based on Scale
- Small-Scale Models
Lightweight models optimized for efficiency on edge devices.
Examples: DistilBERT, TinyBERT. - Large-Scale Models
Massive models with billions of parameters for high-complexity tasks.
Examples: GPT-3, GPT-4, LLaMA 2.
5. Based on Deployment
- Cloud-Based Models
Accessed via APIs for large-scale applications.
Examples: OpenAI’s GPT, Google’s PaLM API. - On-Premise Models
Deployed locally for private use or customization.
Examples: LLaMA, Falcon, GPT-J.
6. Based on Accessibility
- Open-Source Models
Freely available for modification and use.
Examples: GPT-Neo, LLaMA, MPT. - Proprietary Models
Owned by organizations, often offered as paid services.
Examples: GPT-4, Claude by Anthropic.
7. Based on Multilingual Capabilities
- Monolingual Models
Focus on a single language.
Examples: AraBERT (Arabic), FinBERT (English). - Multilingual Models
Handle multiple languages effectively.
Examples: XLM-R, mT5.
8. Specialized Models
- Conversational Models
Optimized for dialogue and conversational AI.
Examples: ChatGPT, LaMDA. - Retrieval-Augmented Models
Incorporate external knowledge retrieval for enhanced factual accuracy.
Examples: RETRO, RAG.