Greedy Decoding is a text generation strategy where the model selects the single token with the highest probability at each step. It is a deterministic method, meaning it will always produce the same output for a given input. While fast and computationally simple, this approach can lead to suboptimal results, as it makes locally optimal choices without considering the overall quality of the sequence, sometimes resulting in repetitive or nonsensical text.
Greedy decoding is the most straightforward and fundamental approach to decoding for sequence generation models, serving as a simple baseline for more complex methods.
While it is a foundational concept, Greedy Decoding is often replaced by more advanced sampling strategies like beam search, top-k sampling, and nucleus sampling (top-p), which can produce more diverse and coherent text. It is still used in situations where deterministic and fast output is a priority, such as in certain translation or code generation tasks.