Which decoding strategy prioritizes the highest probability token at each step, potentially leading to suboptimal overall sequences?

Greedy decoding is the decoding strategy that prioritizes the highest probability token at each step. In this approach, when generating text, the model chooses the single word with the highest predicted probability as the next word in the sequence. While simple and computationally efficient, this method often leads to suboptimal overall sequences because it makes decisions based only on the current step without considering the potential impact on future steps. This can result in the model getting stuck in a local optimum, where each individual word seems likely, but the overall sequence is incoherent or grammatically incorrect. For example, if a model is generating the sentence 'The cat sat on the', and the next most probable word is 'mat', greedy decoding will select 'mat' even if a less probable word like 'table' would lead to a more coherent and contextually appropriate sentence later on. The strategy makes a local best choice at each step, which does not guarantee the best global sequence.