ML Interview Questions

Describe and compare different techniques for anomaly detection in machine learning, focusing on statistical methods, distance-based methods, density-based methods, and isolation-based methods. What are the strengths and weaknesses of each method, and in what situations would each be most appropriate?

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?

Describe the process and components of Reinforcement Learning from Human Feedback (RLHF) in the context of training large language models (LLMs). Discuss how RLHF incorporates key elements such as reward model training and proximal policy optimization (PPO). Furthermore, explore the challenges faced in aligning LLMs with human preferences using RLHF, and evaluate the limitations of this approach. What are some alternative methods being explored for improving alignment in LLMs?

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).