ML Interview Questions

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?

Explain in detail how transformer-based language models, such as GPT, are structured and function. What are the key components involved in their architecture and how do they contribute to the model's ability to understand and generate human language?

Discuss the differences between fine-tuning and prompt engineering when adapting large language models (LLMs). What are the advantages and disadvantages of each approach, and in what scenarios would you choose one over the other?

How does the Transformer architecture function in the context of large language models (LLMs) like GPT, and why is it preferred over traditional RNN-based models? Discuss the key components of the Transformer and their roles in processing sequences, especially in NLP tasks.

Define and discuss the concept of model alignment in the context of large language models (LLMs). How do techniques such as Reinforcement Learning from Human Feedback (RLHF) contribute to achieving model alignment? Why is this important in the context of ethical AI development?