Prompt
Great. We covered tokenisation and temperature. Can you think about some other technical facets of LLMs that might be hard for laypeople to understand. Give me a list of as many as you can think of (ordered alphabetically)
Output
Here's an alphabetically ordered list of technical facets of LLMs that might be challenging for laypeople to understand:
- Attention mechanisms
- Beam search
- Bidirectional encoding
- Computational complexity
- Context window
- Decoder-only vs. encoder-decoder architectures
- Embedding spaces
- Emergent abilities
- Few-shot and zero-shot learning
- Fine-tuning vs. pre-training
- Gradient descent and backpropagation
- Hallucinations
- Hyperparameter optimization
- In-context learning
- Inference optimization techniques
- Layer normalization
- Loss functions
- Model compression techniques
- Multi-head attention
- Neural architecture search
- Parallel processing and distributed training
- Perplexity
- Positional encoding
- Prompt engineering
- Quantization
- Retrieval-augmented generation (RAG)
- Scaling laws
- Self-attention
- Sequence-to-sequence learning
- Softmax function
- Sparse attention
- Tokenization (subword, byte-pair encoding, etc.)
- Transfer learning
- Transformer architecture
- Unsupervised pre-training
These concepts represent various aspects of LLM design, training, and operation that can be complex for non-experts to grasp fully.