← Microsoft Interview Insights
I felt okay here but rambled a bit on beam search.
Structure your answer by first defining each decoding method and its core mechanism, then compare them across the four dimensions (determinism, diversity, fluency, low-probability token handling). Finally, discuss practical scenarios and trade-offs, emphasizing that the choice depends on the application's requirements for creativity vs. reliability.
Pro tip: Mention that in production systems, a hybrid approach like top-p with temperature is often used, and that beam search, while powerful, can be computationally expensive and may produce bland outputs due to its focus on high-probability sequences.
Briefly explain greedy decoding (always pick highest probability token), top-k sampling (sample from top k tokens), top-p sampling (sample from smallest set of tokens whose cumulative probability exceeds p), and beam search (keep top b sequences at each step).
Greedy and beam search are deterministic (given same input), while top-k and top-p are stochastic. Greedy and beam search produce low diversity; top-k and top-p can produce high diversity, with top-p often more coherent due to dynamic token set.
Greedy and beam search tend to produce fluent but possibly repetitive or generic text; they avoid low-probability tokens. Top-k and top-p can introduce low-probability tokens, but top-p adapts better by excluding unlikely tokens when the distribution is peaked, thus maintaining fluency.
Greedy: fast, deterministic, but dull. Beam search: better for tasks with a clear correct answer (e.g., translation), but computationally heavy and can be less diverse. Top-k: simple, but fixed k may be suboptimal. Top-p: often best for open-ended generation, balancing diversity and coherence. Choose based on need for creativity vs. precision, and computational budget.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Easier than I thought conceptually, but I got tripped up tracking the running probability.
Start by clarifying the problem and edge cases, then outline a step-by-step algorithm before coding. Implement the greedy loop, track the product of probabilities, and handle termination conditions. Finally, discuss potential issues like numerical underflow and alternatives like beam search.
Pro tip: Mention that the product of probabilities can underflow, so in practice you'd sum log-probabilities; this shows awareness of numerical stability. Also, note that greedy decoding is fast but can lead to repetitive or suboptimal sequences, so it's often used as a baseline.
Ask about the input format, special tokens (e.g., end-of-sentence marker), maximum length, and whether the probability distribution is normalized. Confirm return types and handling of empty distributions.
Describe the loop: start with an initial token, get the distribution, select the token with the highest probability, append it to the sequence, multiply the probability into a running product, and check for termination (EOS or max length).
Write clean Python code with clear variable names. Use a loop, track the product, and handle the case where the distribution is empty (e.g., break or raise an error).
Walk through a simple example to verify correctness, including edge cases like immediate EOS or reaching max length. Check that the product is computed correctly.
Mention limitations of greedy decoding (e.g., lack of diversity, suboptimal sequences) and alternatives like beam search or sampling. Also discuss numerical stability (log-probabilities) and efficiency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.