LLM

Large Language Models (LLMs)

1. Parameter-Efficient Fine-Tuning (PEFT)

   • Techniques like LoRA (Low-Rank Adaptation) for tuning large models efficiently.

2. Prompt Engineering Strategies

   • Chain-of-thought, few-shot, and zero-shot prompting techniques.

3. Context Management in LLMs

   • Approaches for handling long contexts with memory-efficient attention.

4. Alignment and Safety in LLMs

   • Techniques like RLHF to align models with human intentions.

5. Scaling Laws for LLMs

   • Understanding how scaling model size affects performance and training efficiency.

6. Memory-Augmented LLMs

   • Integrating memory mechanisms to improve recall over long conversations.

7. Embedding Spaces and Representation Learning

   • Techniques for embedding generation and similarity search.

8. Advanced Tokenization Techniques

   • Byte-Pair Encoding, SentencePiece, and how tokenization affects LLM performance.