Start by clarifying requirements and constraints (e.g., n-gram order, smoothing method, corpus size) to scope the problem. Then walk through the pipeline: tokenization, n-gram counting, probability estimation with smoothing, and sampling. Emphasize trade-offs between model complexity, memory, and generation quality, and discuss how to handle unseen contexts.
Pro tip: Mention that you would use add-k smoothing or backoff to handle unseen contexts, and explain how you would evaluate the generator (e.g., perplexity on held-out data) to demonstrate end-to-end thinking.
Ask about the expected n-gram order, corpus size, and whether any smoothing is required. Confirm that no external libraries are allowed and discuss memory/time constraints.
Explain how to tokenize text (e.g., split on whitespace, handle punctuation, lowercase) and build a vocabulary. Mention handling of out-of-vocabulary words and start/end tokens.
Describe counting n-grams (e.g., bigrams, trigrams) using dictionaries or nested maps. Convert counts to probabilities via maximum likelihood estimation, and discuss smoothing techniques (Laplace, backoff) for unseen contexts.
Explain how to sample the next word from the probability distribution (e.g., using cumulative distribution and random number). Discuss strategies for handling unseen contexts during generation (e.g., backoff to lower-order n-grams).
Mention evaluation metrics like perplexity and discuss trade-offs between n-gram order, smoothing, memory usage, and generation quality. Suggest potential improvements like interpolation or neural methods.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by defining n-gram models and their core trade-offs: higher-order n-grams capture more context but suffer from data sparsity and increased memory/compute, while lower-order n-grams are more robust but less expressive. Then, discuss how to transition to neural approaches like feedforward or recurrent neural language models, highlighting how they address sparsity through distributed representations and can incorporate longer context. Finally, tie the discussion to practical considerations for Uber's scale, such as latency, memory, and training efficiency.
Pro tip: Emphasize that in production systems, n-gram models are still relevant for their speed and simplicity, and a hybrid approach (e.g., using n-grams for candidate generation and neural models for ranking) is often pragmatic. This shows you understand real-world trade-offs beyond academic benchmarks.
Explain that n-gram models predict the next word based on the previous n-1 words. Discuss how increasing n improves context but leads to exponential growth in parameters, data sparsity, and overfitting, while decreasing n reduces sparsity but loses long-range dependencies.
Mention specific metrics: perplexity, memory footprint, inference latency, and training data requirements. For example, a trigram model has O(V^3) parameters, which is impractical for large vocabularies, whereas bigram is O(V^2).
Describe how neural language models (e.g., feedforward, RNN, LSTM, Transformer) use distributed representations (embeddings) to generalize across similar contexts, mitigating sparsity. Highlight that they can capture longer dependencies and scale with data.
Discuss neural models' higher computational cost, memory requirements, and need for large datasets, but superior performance and flexibility. Mention techniques like subword tokenization and attention to handle long contexts.
Connect to Uber's needs: real-time prediction (e.g., ETA, rider-driver matching) may favor lightweight n-grams or hybrid systems, while complex tasks like conversational AI could benefit from neural models. Suggest a staged approach: start with n-grams for baseline, then move to neural as data and infrastructure allow.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.