I got the structure right but fumbled the reshape logic for splitting heads.
Start by clarifying the input shapes and the expected output shape, then outline the multi-head attention computation step by step: linear projections, splitting into heads, scaled dot-product attention with masking and dropout, concatenation, and final projection. Emphasize the importance of matching the input sequence shape and handling the optional mask correctly.
Pro tip: Mention that you would use efficient tensor operations (e.g., einsum or reshape/transpose) to avoid explicit loops, and discuss the trade-offs between clarity and performance. Also, note that the scaling factor prevents softmax saturation, which is crucial for stable gradients.
Confirm the shapes of query, key, and value tensors (batch_size, seq_len, d_model) and the number of heads. Ensure the output should have the same shape as the input.
Apply linear projections to query, key, and value to obtain d_model-dimensional representations. Then split each into h heads of dimension d_k = d_model / h, reshaping to (batch_size, h, seq_len, d_k).
Compute attention scores as Q @ K^T / sqrt(d_k). Apply the mask (if provided) by setting masked positions to -inf before softmax. Apply softmax to get attention weights, then apply dropout. Multiply by V to get head outputs.
Concatenate the outputs from all heads along the feature dimension, resulting in (batch_size, seq_len, d_model). Apply a final linear projection to produce the output.
Discuss why scaling by sqrt(d_k) is necessary: it prevents the dot products from growing too large in magnitude, which would push the softmax into regions with tiny gradients, hindering learning.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Sorting the probabilities descending and doing a cumsum felt natural, but I initially forgot to handle the edge case where p equals 1.
Start by outlining the algorithm: compute softmax probabilities from logits, sort tokens by probability descending, find the smallest set where cumulative probability >= p, then renormalize and sample. For the follow-up, compare top-p and top-k in terms of adaptivity, computational cost, and practical performance.
Pro tip: Mention that top-p sampling is often preferred for open-ended generation because it adapts to the model's confidence, but top-k can be more stable for tasks requiring diversity control. Also, note that both can be combined in practice.
Clarify that nucleus sampling selects the smallest set of tokens whose cumulative probability exceeds p, ensuring a dynamic vocabulary size based on the distribution.
Describe steps: compute softmax, sort probabilities, compute cumulative sum, find cutoff index where cumsum >= p, zero out others, renormalize, and sample from the filtered distribution.
Mention handling edge cases (e.g., p=0 or p=1), efficiency considerations (sorting O(V log V)), and potential optimizations like using a heap for large vocabularies.
Explain that top-k uses a fixed number of tokens, while top-p adapts to the distribution's shape. Discuss advantages (top-p: dynamic, avoids including unlikely tokens; top-k: simpler, consistent diversity) and disadvantages (top-p: can include too many tokens if distribution is flat; top-k: may cut off plausible tokens or include implausible ones).
Summarize when to use each method, mention that top-p is common in state-of-the-art LLMs like GPT, and note that hyperparameter tuning is often needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.