Greedy Decoding is a simple and fast text generation strategy where the model always selects the word with the highest probability at each step.
How Greedy Decoding Works:
At each step of text generation, the model picks the most probable next word.
No consideration is given to long-term coherence or fluency.
This method can lead to repetitive and suboptimal outputs due to the lack of exploration.
Limitations of Greedy Decoding:
May miss optimal sentence structures because it only considers the next word, not the full sequence.
Less diversity in generated text, as it follows the highest-probability path rigidly.
Better alternatives exist: Beam Search, Top-k Sampling, and Temperature Scaling provide more refined results.
Why Other Options Are Incorrect:
(A) is incorrect because greedy decoding does not select random words.
(C) is incorrect because word choice is based on probability, not sentence structure.
(D) is incorrect because weighted random selection is used in sampling methods like Top-k or Top-p (nucleus sampling).
🔹 Oracle Generative AI Reference:
Oracle AI incorporates Greedy Decoding, Beam Search, and Stochastic Sampling in its text generation models to optimize for accuracy and diversity.