- Explain the Transformer architecture and how self-attention works; discuss computational complexity and why multi-head attention helps. - Differentiate attention types (self vs. cross; scaled dot-product) and when to use them. - Define causal (autoregressive) decoding and how attention masks enforce causality. - Compare decoding/sampling strategies (greedy, temperature, top-k, nucleus/top-p, beam search); explain trade-offs in quality, diversity, and latency. - Describe how reinforcement learning is used to fine-tune LLMs (e.g., reward modeling, preference data, PPO or alternatives, KL control) and common stability challenges. - Propose an evaluation plan for LLMs (automatic metrics like perplexity/accuracy, task-based eval, human evaluation, safety/robustness), and how to avoid data leakage and ensure statistical significance. - Outline key optimization techniques for training/inference (optimizer and LR schedules, mixed precision, gradient checkpointing, parameter-efficient finetuning like LoRA, and distributed strategies such as DP/TP/ZeRO).

This question evaluates understanding of transformer architectures, self- and cross-attention mechanisms, autoregressive decoding and sampling strategies, reinforcement learning–based fine-tuning (RLHF), evaluation methodologies, and training/inference optimization techniques.

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This is a hard difficulty Machine Learning question, commonly asked during Technical Screen rounds at Scale AI.

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Explain Transformers, attention, decoding, RL, and evaluation

Technical Screen: Transformers, Attention, Decoding, RLHF, Evaluation, and Optimization

Context: Assume a modern decoder-only LLM unless stated otherwise. Address each prompt concisely but precisely, highlighting trade-offs and practical considerations.

Transformer architecture and self-attention

Explain the Transformer block and how scaled dot-product self-attention works.
Analyze computational complexity (time/memory) with respect to sequence length n and hidden size d.
Explain why multi-head attention helps.

Attention types and use cases

Differentiate self-attention vs. cross-attention.
Define scaled dot-product attention and note alternatives.
Explain when each attention type is used.

Causal (autoregressive) decoding and masks

Define causal decoding.
Show how attention masks enforce causality.

Decoding and sampling strategies

Compare greedy, temperature, top-k, nucleus (top-p), and beam search.
Explain trade-offs in quality, diversity, and latency; give practical tuning guidance.

RL-based fine-tuning of LLMs

Describe the RLHF pipeline: preference data, reward modeling, PPO (or alternatives), and KL control.
Discuss common stability challenges and mitigations.

Evaluation plan for LLMs

Propose automatic metrics (e.g., perplexity, accuracy), task-based evaluation, human evaluation, and safety/robustness tests.
Explain how to avoid data leakage and ensure statistical significance.

Optimization techniques for training and inference

Cover optimizers and LR schedules, mixed precision, gradient checkpointing, parameter-efficient finetuning (e.g., LoRA), and distributed strategies (DP/TP/ZeRO/FSDP).
Include key inference optimizations (KV cache, quantization, speculative decoding).

Technical Screen: Transformers, Attention, Decoding, RLHF, Evaluation, and Optimization

Context: Assume a modern decoder-only LLM unless stated otherwise. Address each prompt concisely but precisely, highlighting trade-offs and practical considerations.

Transformer architecture and self-attention

Explain the Transformer block and how scaled dot-product self-attention works.
Analyze computational complexity (time/memory) with respect to sequence length n and hidden size d.
Explain why multi-head attention helps.

Attention types and use cases

Differentiate self-attention vs. cross-attention.
Define scaled dot-product attention and note alternatives.
Explain when each attention type is used.

Causal (autoregressive) decoding and masks

Define causal decoding.
Show how attention masks enforce causality.

Decoding and sampling strategies

Compare greedy, temperature, top-k, nucleus (top-p), and beam search.
Explain trade-offs in quality, diversity, and latency; give practical tuning guidance.

RL-based fine-tuning of LLMs

Describe the RLHF pipeline: preference data, reward modeling, PPO (or alternatives), and KL control.
Discuss common stability challenges and mitigations.

Evaluation plan for LLMs

Propose automatic metrics (e.g., perplexity, accuracy), task-based evaluation, human evaluation, and safety/robustness tests.
Explain how to avoid data leakage and ensure statistical significance.

Optimization techniques for training and inference

Cover optimizers and LR schedules, mixed precision, gradient checkpointing, parameter-efficient finetuning (e.g., LoRA), and distributed strategies (DP/TP/ZeRO/FSDP).
Include key inference optimizations (KV cache, quantization, speculative decoding).

Explain Transformers, attention, decoding, RL, and evaluation

Quick Overview

Technical Screen: Transformers, Attention, Decoding, RLHF, Evaluation, and Optimization

Solution

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Explain Transformers, attention, decoding, RL, and evaluation

Quick Overview

Technical Screen: Transformers, Attention, Decoding, RLHF, Evaluation, and Optimization

Solution

Comments (0)