ML Interview Questions

How do you reduce inference cost for LLMs?

Discuss the differences between encoder-only, decoder-only, and encoder-decoder transformer architectures, focusing on their specific characteristics and potential applications.

Explain the attention mechanism in transformers, focusing on self-attention and multi-head attention. Discuss their importance in the architecture and functioning of transformer models.

Explain how Retrieval-Augmented Generation (RAG) works and its advantages over traditional large language models (LLMs).

Discuss in-context learning within the framework of Large Language Models (LLMs). How does few-shot prompting facilitate model adaptation without updating model parameters? Provide examples of practical applications and challenges associated with this approach.

What are the ethical considerations when deploying large language models (LLMs), specifically focusing on issues such as bias, misinformation, and copyright concerns?

Explain how Reinforcement Learning from Human Feedback (RLHF) is employed to align Large Language Models (LLMs) with human values and intentions.

Discuss the concept of parameter-efficient fine-tuning in the context of large language models (LLMs). Explain techniques such as LoRA, prefix tuning, and adapters, and how they contribute to efficient training and model optimization. What are the advantages and challenges associated with these techniques?

Large Language Models (LLMs) sometimes generate outputs that are factually incorrect or "hallucinate" information that is not present in their training data. Describe advanced techniques that can be used to minimize these hallucinations and enhance the factuality of LLM outputs, particularly focusing on Retrieval-Augmented Generation (RAG).

Explain how you would design an evaluation framework for a large language model (LLM). What metrics would you consider essential, and how would you implement benchmarking to ensure the model's effectiveness across different tasks?