How does stochastic sampling compare with deterministic methods for realistic text generation

Question

Can you explain how stochastic sampling compare with deterministic methods for realistic text generation?

Answer 1

Stochastic sampling introduces randomness, allowing for diverse and creative text outputs.

• Deterministic methods like greedy search or beam search produce predictable and often repetitive results.
• Stochastic methods like top-k or nucleus sampling balance randomness with control for more realistic generation.

Here is the code snippet showing how it is done:

In the above code, we have used the following:

• Greedy Decoding:
  • temperature=0: No randomness; the model selects the most probable word at each step.
  • Result: Safe but predictable and sometimes repetitive.

• Stochastic Sampling:
  • temperature=0.8: Adds controlled randomness for creativity.
  • top_p=0.9: Implements nucleus sampling, focusing on a subset of most likely words (cumulative probability ≤ 0.9).
  •  Result: Diverse and realistic outputs, great for storytelling or creative tasks.

This demonstrates the trade-off between consistency (greedy) and creativity (stochastic).