The recursive version felt natural to write but I second-guessed myself on the base cases.
Clarify the problem constraints and edge cases first, then outline the greedy decoding algorithm before writing code. Implement both recursive and iterative versions, explaining the trade-offs between them. Test with examples including dead ends and terminal tokens.
Pro tip: Mention that greedy decoding is deterministic and can get stuck in loops or produce suboptimal sequences, so in practice you might use beam search or sampling. Also, highlight the importance of handling cycles to avoid infinite loops.
Ask about the dictionary format, terminal token, dead end (no next token), and whether cycles are possible. Confirm if the sequence should include the start token and terminal token.
Explain greedy decoding: at each step, pick the next token with the highest probability. For recursion, define a base case (terminal or dead end) and recursive case. For iteration, use a loop with a visited set to detect cycles.
Write a recursive function that takes the current token and accumulated sequence. If current token is terminal or has no next tokens, return the sequence. Otherwise, select the highest-probability next token and recurse.
Use a while loop: start with the start token, repeatedly look up the next token with max probability, append to sequence, and update current token. Stop when terminal token is reached or no next token exists. Use a visited set to prevent infinite loops.
Compare recursion (elegant but risk of stack overflow) vs iteration (more control, better for long sequences). Discuss time complexity O(n) where n is sequence length, and space complexity. Walk through a small example.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the probability dictionary structure and beam search requirements, then outline a BFS-style expansion that maintains the top k beams at each step. Implement the algorithm with cumulative log-probabilities, track completed sequences, and finally analyze time and space complexity in terms of sequence length, branching factor, vocabulary size, and beam size.
Pro tip: Emphasize the importance of using log-probabilities to avoid numerical underflow and discuss how beam search balances exploration and exploitation, which is crucial for real-world ML systems like Cresta's conversational AI.
Confirm the format of the probability dictionary (e.g., mapping from prefix to next-token probabilities) and define beam size k, max sequence length, and end-of-sequence token. Decide whether to return all completed beams or just the best.
Start with a single beam containing the start token with log-probability 0. Maintain a list of active beams (each with sequence and cumulative log-prob) and a list of completed beams.
For each step up to max length, expand each active beam by considering all possible next tokens from the probability dictionary. Compute new cumulative log-probabilities, then select the top k beams overall to keep. Move beams that hit the end token to completed beams.
After expansion, if completed beams exist, return the one with the highest cumulative log-probability (or all completed beams if requested). If no completed beams, return the best active beam or handle as needed.
Time: O(T * k * b * V) where T is max length, k is beam size, b is branching factor (or V if all tokens considered), and V is vocabulary size. Space: O(k * T) for storing beams, plus O(k * V) for expansion if not careful. Discuss optimizations like pruning low-probability tokens.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.