Start with a high-level overview of the Transformer block, then systematically describe each component (multi-head self-attention, feed-forward network, residual connections, layer normalization) and its function. Emphasize how these components work together to enable parallel processing and capture long-range dependencies, and briefly mention trade-offs like computational complexity.
Pro tip: Relate the components to real-world applications at TikTok, such as how self-attention helps model user interactions across videos, and discuss trade-offs like the quadratic complexity of attention and how techniques like sparse attention or linear approximations can mitigate it.
Briefly state that a Transformer block is the fundamental building unit of the Transformer architecture, consisting of multi-head self-attention, feed-forward network, residual connections, and layer normalization.
Explain that it computes attention scores between all positions in the input sequence, allowing the model to weigh the importance of different tokens. Mention that multiple heads capture diverse relationships.
Describe it as a position-wise fully connected network (typically two linear layers with a ReLU activation) that introduces non-linearity and transforms the attended representations.
Explain that residual connections add the input to the output of each sub-layer to ease gradient flow, and layer normalization stabilizes training by normalizing activations across features.
Summarize how these components are stacked (often with pre- or post-norm) and discuss trade-offs such as computational complexity, memory usage, and parallelizability compared to RNNs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where I probably lost some points.
Start by contrasting the architectural foundations: GPT's decoder-only, autoregressive design versus BERT's encoder-only, bidirectional design. Then explain how pretraining objectives (causal language modeling vs. masked language modeling) shape their capabilities, and finally map these differences to practical use cases like text generation versus understanding tasks.
Pro tip: Emphasize that the choice often depends on whether your task requires generation or understanding, but also mention hybrid approaches (e.g., using BERT for retrieval and GPT for generation) to show depth. At TikTok, where content understanding and generation both matter, this nuance is valuable.
Explain that GPT uses a decoder-only Transformer with causal (left-to-right) attention, while BERT uses an encoder-only Transformer with bidirectional attention. Mention that GPT is autoregressive, generating one token at a time, whereas BERT processes the entire input simultaneously.
Describe GPT's pretraining as causal language modeling (predict next token) and BERT's as masked language modeling (predict randomly masked tokens) plus next sentence prediction. Highlight how these objectives lead to different strengths: GPT excels at generation, BERT at understanding context.
Discuss how GPT is typically fine-tuned for generation tasks (e.g., summarization, dialogue) and BERT for discriminative tasks (e.g., classification, named entity recognition). Mention that BERT's bidirectional context helps with tasks requiring deep understanding, while GPT's sequential nature suits open-ended generation.
Outline when to choose one over the other: BERT for tasks where full context is needed and generation is not required (e.g., sentiment analysis, question answering), GPT for tasks requiring coherent text generation or few-shot learning. Consider factors like latency, model size, and computational resources.
Provide examples: BERT for content moderation or search ranking, GPT for chatbots or content creation. Mention that in practice, hybrid systems (e.g., BERT for retrieval, GPT for response generation) are common, especially in large-scale applications like TikTok.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clearly defining precision and recall with their formulas, then explain how the threshold controls the precision-recall tradeoff. Use a concrete example (e.g., TikTok content moderation) to illustrate when to prioritize each metric, and tie it back to business impact.
Pro tip: Mention that the choice of metric should be driven by the relative cost of false positives vs. false negatives, and that in practice you often tune the threshold to meet a specific precision or recall target rather than optimizing a single metric.
Precision = TP / (TP + FP), the fraction of positive predictions that are correct. Recall = TP / (TP + FN), the fraction of actual positives that are correctly identified.
Lowering the threshold classifies more items as positive, typically increasing recall but decreasing precision. Raising the threshold does the opposite, increasing precision but decreasing recall.
Use a TikTok-specific scenario, such as content moderation: high recall to catch all harmful content (even if some false positives), or high precision to avoid over-removing benign content.
Prioritize recall when false negatives are costly (e.g., missing a violation), and precision when false positives are costly (e.g., wrongly banning a user). Mention that the optimal threshold depends on the business context.
Summarize that the threshold is a tunable parameter to balance precision and recall, and that in practice you might use metrics like F1 or PR-AUC to evaluate overall performance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.