AI Core Concepts (Part 8): Large Language Models (LLMs)
Large Language Models (LLMs) are deep learning models trained on massive corpora of text to understand and generate human-like language. They are used in chatbots, summarization, code generation, translation, and more.
1. What Makes a Language Model "Large"?
- Scale: Billions to trillions of parameters.
- Data: Trained on terabytes of multilingual, web-scale text data.
- Architecture: Based on the Transformer, especially decoder-only versions.
- Capabilities: Few-shot and zero-shot learning, in-context understanding, reasoning.
Popular examples:
- GPT-3, GPT-4
- Claude
- LLaMA
- Mistral
- Gemini
2. Pretraining and Finetuning
Pretraining
- Unsupervised: Predict next word/token (causal or masked).
- Objective: Maximize likelihood of token sequences.
- Requires massive compute (hundreds of GPUs/TPUs).
Finetuning
- Models are adapted to specific domains or tasks using labeled or curated data.
- Often includes techniques like LoRA or QLoRA for efficiency.
Example: Finetuning a model using Hugging Face Trainer
model = AutoModelForCausalLM.from_pretrained("gpt2") training_args = TrainingArguments(output_dir="./model", per_device_train_batch_size=4)
trainer = Trainer(model=model, args=training_args, train_dataset=my_dataset) trainer.train()
3. Inference and Text Generation
LLMs can complete, summarize, or translate text using autoregressive decoding.
Example: Using GPT-2 for text generation
generator = pipeline("text-generation", model="gpt2") result = generator("The future of AI is", max_length=30) print(result[0]["generated_text"])
4. Applications of LLMs
- 🧠 Question Answering
- 💬 Chatbots and virtual assistants
- 📝 Summarization and content generation
- 🔍 Semantic Search
- 🧑💻 Code generation (e.g., Copilot, Codex)
- 🧾 Document classification and parsing
5. LLM Challenges and Solutions
| Challenge | Solution/Technique |
|---|---|
| Hallucinations | Post-processing, retrieval augmentation |
| Prompt sensitivity | Prompt engineering, prompt tuning |
| Compute cost | Quantization, LoRA, distillation |
| Privacy & bias issues | RLHF, filtering datasets, transparency |
6. Popular Tools and Frameworks
- 🤗 Transformers (Hugging Face)
- 🔧 LangChain – for building LLM pipelines and agents
- 🧠 LLamaIndex – for retrieval-augmented generation (RAG)
- 🔍 OpenAI API
- 🐍 Text-generation-webui
📚 Further Resources
<< back to Guides