I started with the encoder side and worked my way through, but I fumbled when they pushed on cross-attention specifically.
Start with a high-level overview of the Transformer architecture, then systematically explain the encoder and decoder stacks, self-attention, cross-attention, and feed-forward networks. Use clear analogies and emphasize how each component contributes to the model's ability to handle sequence-to-sequence tasks.
Pro tip: Connect each component to its practical benefits, such as parallelization and handling long-range dependencies, and mention trade-offs like quadratic complexity of self-attention. This shows you understand both theory and real-world implications.
Introduce the Transformer as a sequence-to-sequence model that relies entirely on attention mechanisms, eliminating recurrence and convolutions. Mention its key advantages: parallelization and capturing long-range dependencies.
Describe the encoder as a stack of identical layers, each with two sub-layers: multi-head self-attention and position-wise feed-forward network. Explain that each sub-layer has residual connections and layer normalization.
Explain the decoder stack, also composed of identical layers, but with three sub-layers: masked multi-head self-attention, multi-head cross-attention over encoder outputs, and feed-forward network. Emphasize masking to prevent attending to future tokens.
Detail how self-attention computes queries, keys, and values from the same input, while cross-attention uses queries from the decoder and keys/values from the encoder. Explain scaled dot-product attention and multi-head attention.
Describe the position-wise feed-forward network as two linear transformations with a ReLU activation in between, applied identically to each position. Mention positional encodings added to input embeddings to inject sequence order.
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Wrote out softmax(QK^T / sqrt(d_k))V and got the shapes right for the most part.
Start by deriving scaled dot-product attention from first principles, clearly defining the query, key, and value matrices and their shapes. Then explain how multi-head attention extends this by projecting the inputs into multiple subspaces and concatenating the results. Use a concrete example with dimensions to illustrate the shapes and computations.
Pro tip: Emphasize the scaling factor 1/sqrt(d_k) as a variance control mechanism to prevent softmax saturation, and relate multi-head attention to ensemble learning for richer representations.
State that for a sequence of length n and model dimension d, the input X has shape (n, d). The query, key, and value matrices are obtained via learned linear projections: Q = X W_Q, K = X W_K, V = X W_V, where W_Q, W_K, W_V have shapes (d, d_k), (d, d_k), and (d, d_v) respectively, yielding Q: (n, d_k), K: (n, d_k), V: (n, d_v).
Compute attention scores as the dot product of Q and K^T, giving a matrix of shape (n, n). Scale by 1/sqrt(d_k) to stabilize gradients. Apply softmax row-wise to obtain attention weights, then multiply by V to get the output of shape (n, d_v).
Describe how multi-head attention runs h parallel attention heads, each with its own learned projections W_Q^i, W_K^i, W_V^i of shapes (d, d_k), (d, d_k), (d, d_v) where typically d_k = d_v = d/h. Each head produces an output of shape (n, d_v).
Concatenate the outputs of all heads along the feature dimension to get a matrix of shape (n, h * d_v) = (n, d). Apply a final linear projection W_O of shape (d, d) to produce the final output of shape (n, d).
Highlight that multi-head attention allows the model to jointly attend to information from different representation subspaces, improving expressiveness. Mention computational cost: total complexity is O(n^2 d) for both single and multi-head, but multi-head enables parallelization and diverse attention patterns.
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Start by defining sinusoidal and learned positional encodings, highlighting their absolute nature and trade-offs. Then explain relative position schemes, contrasting them with absolute methods. Finally, connect these to how models understand sequence order, emphasizing implications for generalization and efficiency.
Pro tip: Mention that relative schemes like Shaw et al. and T5 are crucial for long sequences and extrapolation, and that modern models like Transformer-XL and DeBERTa use them to achieve state-of-the-art results.
Explain that sinusoidal encodings use fixed sine/cosine functions of different frequencies, while learned encodings are trainable embeddings added to inputs. Both are absolute, encoding position independently.
Discuss how sinusoidal encodings generalize to unseen lengths and require no parameters, while learned encodings can adapt to data but may overfit and struggle with longer sequences.
Describe relative schemes that encode pairwise distances between tokens, such as Shaw et al., Transformer-XL, and T5. Highlight their ability to capture relative order and generalize better.
Connect these encodings to how models process order: absolute encodings provide a global reference, while relative encodings focus on local relationships, affecting tasks like language modeling and translation.
Conclude with when to use each: sinusoidal for simplicity and extrapolation, learned for flexibility with ample data, and relative for long sequences and tasks requiring relative order.
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Start by defining each architecture in terms of its attention pattern and training objective, then contrast their typical use cases and trade-offs. Explain masking as a causal constraint that prevents attending to future tokens, and connect it to autoregressive generation and training efficiency.
Pro tip: Emphasize that masking is not just about preventing information leakage during training but also enables parallel training of autoregressive models, a key insight for system design at scale.
Briefly describe encoder-only (bidirectional attention, e.g., BERT), decoder-only (causal attention, e.g., GPT), and encoder-decoder (bidirectional encoder + causal decoder, e.g., T5).
Explain how encoder-only uses full self-attention for masked language modeling, decoder-only uses causal masking for next-token prediction, and encoder-decoder combines both for sequence-to-sequence tasks.
Highlight that encoder-only excels at understanding tasks, decoder-only at generation, and encoder-decoder at conditional generation; mention computational and latency differences.
Describe how a causal mask (upper triangular) ensures each position attends only to previous positions, preventing information leakage during training and enabling parallel training.
Note that during inference, masking is implicit via sequential generation, and discuss how this impacts caching, batching, and latency in production systems.
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Talked through sparse attention patterns and linear attention approximations.
Start by acknowledging the quadratic bottleneck and its implications, then categorize approaches into sparse/approximate attention, recurrence/compression, and kernel/low-rank methods. For each category, explain the core idea and discuss trade-offs in terms of complexity, memory, accuracy, and hardware efficiency.
Pro tip: Emphasize that many efficient Transformers trade theoretical complexity for practical speed, and that hardware-aware implementations (e.g., FlashAttention) often matter more than asymptotic improvements. Mention that Google's own work (e.g., Performer, Reformer, BigBird) is highly relevant.
Briefly explain why O(n²) is a bottleneck: memory and compute grow quadratically, limiting sequence length. Mention that this affects both training and inference.
Group methods into: (a) sparse/approximate attention (e.g., Longformer, BigBird, Reformer), (b) recurrent/compressive (e.g., Transformer-XL, Compressive Transformer), (c) kernel/low-rank (e.g., Performer, Linformer), and (d) hardware-optimized exact attention (e.g., FlashAttention).
For each category, describe the key idea, complexity, and a representative model. For example, sparse attention reduces complexity to O(n) or O(n log n) by attending to a subset of tokens.
For each approach, highlight trade-offs: reduced expressiveness, approximation error, implementation complexity, memory vs. compute trade-offs, and hardware efficiency. Compare to full attention.
Summarize that the choice depends on the task, sequence length, and hardware. Mention that hybrid approaches and hardware-aware methods are often preferred in practice.
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Pre-LN stabilizes training and is what most modern models use, post-LN is the original paper but can have gradient issues at depth.
Start by defining pre-LN and post-LN architectures, then compare their training dynamics, stability, and initialization requirements. Use the residual connection as the central mechanism to explain why pre-LN enables stable training without warmup, while post-LN often needs careful initialization and learning rate warmup. Conclude with practical trade-offs and when each is preferred.
Pro tip: Mention that pre-LN Transformers are easier to train but can underperform post-LN in final quality if not tuned, and that post-LN with proper initialization (e.g., Fixup or T-Fixup) can match pre-LN stability. This shows depth beyond textbook knowledge.
Clearly describe pre-LN (LayerNorm before sublayer) and post-LN (LayerNorm after residual addition) placements, and note that original Transformer used post-LN.
Discuss how in pre-LN, the residual path is clean and gradients flow directly, while in post-LN, LayerNorm is inside the residual branch, affecting gradient scale.
Explain that post-LN requires careful initialization (e.g., smaller weights) and learning rate warmup to avoid divergence, whereas pre-LN is more robust and often needs no warmup.
Note that pre-LN trains faster and more stably but may have slightly worse final performance; post-LN can achieve better results with more tuning. Mention that many modern models use pre-LN for ease.
Summarize when to choose each: pre-LN for large-scale, resource-constrained training; post-LN when maximum quality is needed and resources for tuning are available.
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This was the curveball I did not see coming.
Start by clarifying the molecular representation (SMILES vs. graphs) and the task (property prediction, generation, etc.), then walk through the key adaptations: tokenization, handling stereochemistry, data augmentation, and training objectives. Emphasize trade-offs between sequence-based and graph-based approaches, and connect choices to real-world constraints like data scarcity and compute.
Pro tip: Mention that stereochemistry is often overlooked but critical—use specialized tokens or graph features to encode chirality, and consider 3D conformers when relevant. Also, highlight that augmentation must preserve chemical validity, not just string permutations.
Ask about the specific task (e.g., property prediction, generation) and whether to use SMILES strings or molecular graphs. This determines the tokenization and model architecture.
For SMILES, use character-level or atom-level tokenization with special tokens for stereochemistry (e.g., @, /, \). For graphs, define node features (atom type, charge, chirality) and edge features (bond type, direction).
Incorporate stereochemical information via dedicated tokens (SMILES) or node/edge features (graphs). Consider 3D conformers if the task requires spatial awareness.
Use SMILES enumeration (randomized SMILES) and graph augmentation (node/edge dropout, subgraph sampling) that preserve chemical validity. Avoid augmentations that alter the molecule's identity.
For generation, use autoregressive language modeling or masked language modeling (like BERT) on SMILES. For property prediction, use supervised regression/classification. For graphs, consider contrastive learning or masked atom prediction.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.