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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Explain prompt engineering considerations for few-shot/zero-shot generation with large pretrained Transformers. Discuss prompt formatting, context window limits, demonstration selection, and risks like prompt injection or hallucination in production.
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You need to train a Transformer-based language model on a low-resource dataset for a specialized legal domain. Outline a fine-tuning strategy using a pre-trained model, including data preprocessing, tokenization choices, learning rate schedule, regularization, and validation metrics to watch for overfitting.
HardTechnical
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You're asked to migrate a legacy BiLSTM-based NER pipeline to a Transformer architecture. As the AI Engineer lead, create a migration plan covering model selection, data conversion, integration testing, performance regression tests, deployment strategy, and stakeholder communication.
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What is positional encoding in Transformers and why is it needed? Describe two different approaches to inject positional information (sinusoidal and learned embeddings) and trade-offs when using each in production.
MediumTechnical
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A deployed sequence labeling model (NER) built with BiLSTM-CRF shows degraded F1 on entity types appearing rarely in training. Propose a production plan to improve rare-entity performance: data, model, training, and evaluation steps with prioritized actions.
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