← Bloomberg Interview Insights
Started with the greedy approach since it's the obvious move: just keep picking the highest-probability next token until you hit an end-of-sentence marker.
Start by clarifying the problem: given a prefix, generate a full sentence using a bigram model. Then outline a step-by-step algorithm: tokenize, look up next word probabilities, sample, and repeat until an end token or max length. Finally, discuss tradeoffs like fluency vs. diversity, handling unknown words, and computational complexity.
Pro tip: Mention that bigram models often produce repetitive or incoherent sentences, so you might add techniques like temperature scaling or backoff to improve quality. Also, note that Bloomberg values practical solutions, so emphasize how you'd handle real-world constraints like latency and memory.
Confirm the input (a prefix string) and output (a complete sentence). Ask about constraints: maximum length, whether to stop at punctuation, and if the model should be deterministic or probabilistic.
Tokenize the prefix, then iteratively predict the next word using the bigram probabilities. Sample from the distribution (or take argmax) and append until an end token or max length is reached.
Explain how to store the bigram model (e.g., dictionary of dictionaries with counts or probabilities). Mention handling of unknown words (e.g., <UNK> token) and smoothing techniques.
Compare greedy vs. sampling: greedy is deterministic but repetitive; sampling adds diversity but may produce incoherent text. Discuss memory vs. speed, and the impact of smoothing on quality.
Propose enhancements like temperature scaling, backoff to unigram, or using a trigram model. Mention evaluation metrics like perplexity or human judgment.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.