I knew this cold but fumbled the complexity part.
Start by defining Q, K, V as learned linear projections of the input, then explain the scaled dot-product attention formula step by step, including the scaling factor, masking, and softmax. Finally, analyze the computational complexity in terms of sequence length and dimension, highlighting the quadratic bottleneck.
Pro tip: Emphasize that the scaling factor 1/sqrt(d_k) prevents the dot products from growing too large, which would push softmax into regions with tiny gradients, hindering training. Also, mention that masking is crucial for autoregressive decoding and padding, and that complexity is O(n^2 d), which motivates efficient attention variants.
Explain that for each input token, we compute query, key, and value vectors via learned weight matrices W_Q, W_K, W_V. These represent what the token is looking for, what it offers, and what it actually communicates.
Compute dot products between queries and keys to get raw attention scores, then scale by 1/sqrt(d_k) to stabilize gradients. Explain that without scaling, large dot products saturate softmax.
For autoregressive or padded sequences, apply a mask by setting masked positions to -inf before softmax, ensuring they receive zero attention weight.
Apply softmax over the scaled scores to get attention weights that sum to 1. Then compute the output as a weighted sum of value vectors using these weights.
Discuss that the main cost is O(n^2 d) for computing QK^T and softmax, and O(n^2 d) for the weighted sum, where n is sequence length and d is dimension. Highlight that this quadratic scaling with sequence length is a key limitation.
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Start with a high-level overview of the Transformer architecture, then systematically describe the encoder and decoder stacks, emphasizing the placement of residual connections, layer normalization, and positional encoding. Use a whiteboard or verbal diagram to illustrate the flow, and connect each component to its purpose in enabling deep, parallelizable sequence modeling.
Pro tip: Relate the design choices to practical benefits: residual connections and layer normalization enable stable training of deep models, while positional encodings allow the model to handle sequence order without recurrence. Mention that Amazon values scalability and efficiency, so highlight how these components contribute to training speed and model performance.
Introduce the Transformer as an encoder-decoder model that relies solely on attention mechanisms, enabling parallel processing. Briefly state that both encoder and decoder are stacks of identical layers.
Describe the encoder layer: multi-head self-attention followed by a position-wise feed-forward network. Explain that each sub-layer is wrapped with a residual connection and layer normalization.
Describe the decoder layer: masked multi-head self-attention, multi-head cross-attention over encoder outputs, and a feed-forward network. Again, each sub-layer has residual connections and layer normalization.
Explain that residual connections add the input of a sub-layer to its output, mitigating vanishing gradients. Layer normalization is applied after each sub-layer (post-norm) or before (pre-norm), stabilizing training.
Describe how positional encodings (e.g., sinusoidal or learned) are added to input embeddings at the bottom of both encoder and decoder stacks, providing sequence order information.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by contrasting the pretraining objectives: BERT's masked language modeling (MLM) vs GPT's causal language modeling (CLM). Then explain how these objectives drive architectural differences (bidirectional encoder vs unidirectional decoder) and typical downstream usage (fine-tuning for understanding tasks vs prompting/fine-tuning for generation). Finally, discuss the implications for transfer learning and inference behavior, emphasizing trade-offs in latency, memory, and adaptability.
Pro tip: Relate the design choices to practical engineering considerations at Amazon, such as inference cost, scalability, and suitability for different production use cases (e.g., real-time generation vs batch classification).
Explain BERT's masked language modeling (predicting masked tokens) and GPT's causal language modeling (predicting next token). Highlight that BERT sees bidirectional context, while GPT only sees left-to-right context.
Describe BERT as an encoder-only transformer with full self-attention, and GPT as a decoder-only transformer with masked self-attention. Mention parameter counts and typical sizes (e.g., BERT-base vs GPT-3).
Discuss how BERT is typically fine-tuned with task-specific heads for classification, QA, etc., while GPT is used for generation via prompting or fine-tuning. Note that BERT excels at understanding tasks, GPT at generation tasks.
Explain that BERT's bidirectional representations transfer well to tasks requiring deep context understanding, while GPT's generative pretraining transfers well to tasks requiring coherent text generation. Mention that GPT can perform few-shot learning without fine-tuning.
Compare inference: BERT processes the entire input at once (parallel), leading to lower latency for classification; GPT generates tokens sequentially, causing higher latency but enabling flexible generation. Discuss memory and compute trade-offs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.