MLUME

Interview Questions

3 min read 515 words

Fundamentals / Commonly Asked Questions

What is a Large Language Model, and how does it differ from traditional NLP models?
Can you explain how the Transformer architecture works at a high level?
What is the role of attention in LLMs, and why was it such a breakthrough?
How do tokenization and embeddings influence the performance of an LLM?
What is the difference between pre-training and fine-tuning in the context of LLMs?
How does an autoregressive LLM generate text step by step?

Walk me through how positional encodings allow Transformers to handle sequences.
How does the concept of context length affect an LLM’s performance and limitations?
Can you explain the difference between masked language modeling (MLM) and causal language modeling (CLM)?
How do LLMs capture long-range dependencies better than RNNs or LSTMs?
What are some common techniques used to prevent overfitting when training large models?
Why is gradient checkpointing useful when training LLMs at scale?

Why might an LLM produce factually incorrect but fluent-sounding answers?
Imagine you fine-tune an LLM on a small domain-specific dataset, and performance worsens. What could have gone wrong?
Why might a model trained on large-scale internet data generate biased or harmful outputs?
In production, why could inference latency explode even if your model works fine offline?
What challenges arise when deploying an LLM in a multilingual setting?
Suppose you prompt an LLM with incomplete or adversarial instructions. How might the outputs behave, and why?

Compare scaling up model parameters vs scaling up training data. Which gives better performance improvements and why?
Contrast fine-tuning, prompt-tuning, and LoRA (Low-Rank Adaptation). When would you use each?
Compare autoregressive LLMs with encoder-only and encoder-decoder architectures. What trade-offs exist?
How would you weigh the trade-offs between retrieval-augmented generation (RAG) and fine-tuning for domain adaptation?
What are the trade-offs between using greedy decoding, beam search, and sampling-based decoding strategies?
How do latency, cost, and accuracy trade off when deploying LLMs at scale?

What are the core contributions of the “Attention is All You Need” paper?
Explain the scaling laws of LLMs. How do compute, parameters, and data interact?
What role does reinforcement learning with human feedback (RLHF) play in aligning LLMs with human values?
Can you explain Mixture-of-Experts (MoE) architectures and why they are useful in scaling LLMs?
How do recent retrieval-augmented methods (like RAG) extend the capabilities of LLMs?
Discuss the limitations of current evaluation benchmarks (e.g., MMLU) for LLMs.

How would you design an LLM system that minimizes hallucinations while maintaining fluency?
Imagine you are tasked with reducing the carbon footprint of training an LLM. How would you approach it?
How would you architect a system where an LLM continuously learns from new data without catastrophic forgetting?
What strategies would you explore to handle context windows beyond current transformer limits (e.g., 1M+ tokens)?
If you were to design the next generation of LLMs beyond Transformers, what directions would you explore?
How would you approach making LLMs interpretable at scale for high-stakes applications (e.g., healthcare, law)?