LLM Abbreviations Glossary
A quick reference for common abbreviations in the LLM (Large Language Model) domain.
Training & Techniques#
| Abbreviation | Full Name | What It Is |
|---|---|---|
| SFT | Supervised Fine-Tuning | Training on curated question-answer pairs |
| RL | Reinforcement Learning | Learning by trial and reward signals |
| RLHF | Reinforcement Learning from Human Feedback | RL where humans rank outputs to guide training |
| DPO | Direct Preference Optimization | Simpler alternative to RLHF, no reward model needed |
| GRPO | Group Relative Policy Optimization | RL technique used in reasoning models (DeepSeek) |
| PPO | Proximal Policy Optimization | Popular RL algorithm for training LLMs |
| LoRA | Low-Rank Adaptation | Memory-efficient fine-tuning technique |
| QLoRA | Quantized LoRA | LoRA + 4-bit quantization for even less memory |
Architecture & Models#
| Abbreviation | Full Name | What It Is |
|---|---|---|
| LLM | Large Language Model | The models like GPT, Claude, Llama |
| NLP | Natural Language Processing | Field of AI dealing with human language |
| RNN | Recurrent Neural Network | Older architecture before Transformers |
| LSTM | Long Short-Term Memory | Improved RNN that handles longer sequences |
| GRU | Gated Recurrent Unit | Simplified version of LSTM |
| MLP | Multilayer Perceptron | Basic fully-connected neural network |
| GPT | Generative Pre-trained Transformer | OpenAI’s model architecture |
| MoE | Mixture of Experts | Architecture where only some “experts” activate per token |
| MQA | Multi-Query Attention | Attention optimization sharing key-value heads |
| GQA | Grouped-Query Attention | Middle ground between MHA and MQA |
| MHA | Multi-Head Attention | Standard attention with multiple heads |
Applications & Deployment#
| Abbreviation | Full Name | What It Is |
|---|---|---|
| RAG | Retrieval-Augmented Generation | Combining LLMs with external knowledge retrieval |
| API | Application Programming Interface | Way to access LLMs over the internet |
| VRAM | Video Random Access Memory | GPU memory needed to run models |
| MCP | Model Context Protocol | Standard for connecting LLMs to external tools |
| A2A | Agent-to-Agent Protocol | Standard for agent interoperability |
Evaluation & Benchmarks#
| Abbreviation | Full Name | What It Is |
|---|---|---|
| MMLU | Massive Multitask Language Understanding | Popular benchmark for testing LLMs |
| CoT | Chain-of-Thought | Prompting technique for step-by-step reasoning |
| PRM | Process Reward Model | Model that scores intermediate reasoning steps |
Quantization & Optimization#
| Abbreviation | Full Name | What It Is |
|---|---|---|
| GGUF | GPT-Generated Unified Format | File format for quantized models (llama.cpp) |
| GPTQ | GPT Quantization | Post-training quantization method |
| AWQ | Activation-aware Weight Quantization | Quantization preserving important weights |
| FP16 | 16-bit Floating Point | Half-precision number format |
| FP32 | 32-bit Floating Point | Full-precision number format |
| INT8 | 8-bit Integer | Low-precision integer format |
Data & Preprocessing#
| Abbreviation | Full Name | What It Is |
|---|---|---|
| BoW | Bag-of-Words | Text representation ignoring word order |
| TF-IDF | Term Frequency-Inverse Document Frequency | Text weighting technique |
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