Neural Network Architectures: Recurrent & Sequence Models Questions
Comprehensive understanding of RNNs, LSTMs, GRUs, and Transformer architectures for sequential data. Understand the motivation for each (vanishing gradient problem, LSTM gates), attention mechanisms, self-attention, and multi-head attention. Know applications in NLP, time series, and other domains. Discuss Transformers in detail—they've revolutionized NLP and are crucial for generative AI.
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Describe beam search for autoregressive decoding used in translation or summarization. Explain beam width, score tracking, length normalization, handling of completed hypotheses (end-of-sequence tokens), and practical mitigations for loops or repeated text in generated output.
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A transformer must process sequences of length 65,000 tokens (e.g., genomic or long-document data). Discuss architectural and engineering approaches to handle such long sequences efficiently and practically: sparse/linear attention variants, chunking/strided attention, memory-compressed attention, recurrent memory layers, and trade-offs in accuracy, compute, and implementation complexity.
MediumTechnical
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Discuss token-level cross-entropy loss versus sequence-level objectives such as BLEU, ROUGE, or task reward (trained via policy gradient). Explain when optimizing sequence-level objectives is necessary in production and how techniques like minimum-risk training or RL fine-tuning are integrated into pipelines.
MediumTechnical
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A model's BLEU improved after switching tokenizers, but user complaints about output quality increased. Explain possible reasons tokenization changes might improve automated metrics while degrading perceived quality. Describe an investigation plan to validate the root cause and steps to remedy the issue.
MediumTechnical
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You have variable-length sequences batched together with padding. Explain practical strategies to handle padding and masking during training and inference for both RNNs and Transformer models. Cover pack/pad utilities, attention masks, loss masking, avoiding wasted compute, and batching heuristics.
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