Explain Transformer Internals and Implement Top-p Decoding
Company: Mistral AI
Role: Machine Learning Engineer
Category: Machine Learning
Difficulty: hard
Interview Round: Technical Screen
You are interviewing for an AI Scientist role. Explain the Transformer architecture in detail, including attention, positional encoding, encoder versus decoder usage, tokenization, and the shape flow through Q/K/V projections. Then describe how you would implement top-p sampling with softmax during LLM decoding.
### Constraints & Assumptions
- Focus on decoder-only LLMs unless the interviewer asks for encoder or seq2seq details.
- Use tensor shapes where useful.
- Top-p sampling should operate on one step of logits.
- Do not assume a specific framework beyond array/tensor operations.
### Clarifying Questions to Ask
- Should the explanation cover training, inference, or both?
- Are we using absolute positions, RoPE, or another relative position method?
- Should top-p sampling include temperature and top-k?
- Should the implementation support batched decoding?
### What a Strong Answer Covers
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### Follow-up Questions
- How does FlashAttention reduce memory traffic?
- What changes for encoder-decoder attention?
- How would top-p interact with temperature?
- What bugs can make decoding nondeterministic or degenerate?
Quick Answer: This Mistral AI machine learning question evaluates transformer fundamentals and the ability to implement top-p decoding from model outputs. It prepares candidates to explain attention, token sampling, and inference behavior at a level expected in applied LLM interviews.