A greedy algorithm solves a problem by making the choice that looks best at each individual step, without considering how that choice might affect later steps or the overall outcome. In natural language processing specifically, greedy decoding generates text by picking the single highest-probability next word at every step, one word at a time, rather than weighing multiple possible sequences together.
This approach is fast and computationally cheap, since it never needs to track more than one candidate at a time, but it can produce lower-quality results than more thorough methods like beam search, since committing early to the locally best word can rule out a better overall sentence that would have required a slightly less obvious choice earlier on. Greedy algorithms show up well beyond language generation too, in classic computer science problems like pathfinding and scheduling, wherever a fast, "good enough" approximate solution is preferable to an exhaustive search for the optimal one.
