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Natural Language Processing Systems • Text Generation (Beam Search, Sampling, Decoding)Hard⏱️ ~2 min

How to Choose: Deterministic vs Stochastic Decoding

The Core Trade-off
Deterministic methods (greedy, beam search) produce the same output every time for the same input. Stochastic methods (sampling with temperature) produce different outputs each time. The choice depends entirely on your use case: do you need consistency or variety?
Deterministic outputs are verifiable and reproducible. If a user reports a bug, you can regenerate the exact same output. Stochastic outputs cannot be reproduced exactly, which complicates debugging and testing.
When to Use Deterministic Decoding
Machine translation: There is typically one correct translation. Beam search with beam 4-5 consistently outperforms sampling on BLEU scores. Users expect the same translation for the same input.
Summarization: The summary should capture key points reliably. Beam search ensures consistent extraction of important information. Sampling might miss crucial details on some runs.
Code generation from specs: Given identical requirements, generate identical code. Developers need reproducible outputs for testing and version control.
When to Use Stochastic Decoding
Creative writing: Users want variety. The same prompt should yield different stories. Temperature 0.8-1.0 with top_p 0.95 provides diverse yet coherent outputs.
Chatbots and assistants: Slight variation feels more human. Identical responses to repeated questions feel robotic. Temperature 0.7 with top_p 0.9 balances consistency with naturalness.
Brainstorming: Generate multiple distinct options for the user to choose from. Run the same prompt 5 times with temperature 1.0 to get 5 different ideas.
Decision Framework
🎯 Decision Criteria: Is there one correct answer? Use deterministic. Is variety valuable? Use stochastic. Need reproducibility for debugging? Use deterministic with logged seeds. Need A/B testing different outputs? Use stochastic with tracked seeds.

💡 Key Takeaways

✓Deterministic (greedy, beam) gives same output every time; stochastic (sampling) varies each run

✓Deterministic outputs are reproducible for debugging; stochastic cannot be exactly reproduced

✓Use deterministic for translation, summarization, code generation where one answer is correct

✓Use stochastic for creative writing, chatbots, brainstorming where variety is valuable

✓For reproducibility with variation, use stochastic with logged random seeds

📌 Interview Tips

1Match decoding to use case: translation needs beam search, creative writing needs sampling

2Explain debugging trade-off: deterministic outputs can be reproduced, stochastic cannot

3Mention seed logging for A/B testing: track random seeds to reproduce specific outputs

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