Easy interview questions in LLMs & Generative AI, page 2
Red teaming LLMs and harmful output categories
Red teaming deliberately probes a model to elicit harmful behavior; categories include toxic or hateful content and dangerous instructions like weapons or malware.
Model quantization benefits and risks
Quantization stores weights and activations in lower precision like INT8 or INT4; benefits are smaller memory and faster, cheaper inference; risk is accuracy loss.
How the KV cache speeds transformer generation
Cache stores past keys and values so each new token only computes its own K, Q, V instead of recomputing all prior tokens, cutting per-step cost from quadratic to linear.
Teacher-student knowledge distillation
A small student learns to mimic a large teacher's soft probability outputs, not just hard labels; goal is a compact model retaining most capability.
Rule-based versus model-based LLM guardrails
A guardrail is a programmatic check constraining LLM I/O; rule-based uses regex or blocklists, model-based uses a classifier like a moderation model to detect harmful content.
Fine-tuning vs RAG for daily-updated docs
Choose RAG because docs change daily, embed and index chunks in a vector store, retrieve top matches and inject into the prompt.
Closed API vs open-weight models for production
APIs offer top quality and zero ops but recurring per-token cost and data-sharing concerns, open weights give control, privacy, and tuning at the price of hosting and MLOps burden.
Hugging Face Hub, transformers, and datasets
The Hub hosts models and data, transformers loads models and tokenizers and provides the Trainer, datasets streams and maps preprocessing.
What is the role of temperature in token sampling?
This tests your understanding of how to control the creativity and randomness of a language model's output. A great answer explains that temperature is a divisor applied to the model's logits before the softmax function. Low temperature makes the output more deterministic by sharpening the probability distribution, while high temperature increases randomness by flattening it. A common red flag is vaguely saying it 'controls randomness' without explaining the underlying softmax mechanism.
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