How does beam search enhance the decoding process in sequence-to-sequence models compared to greedy decoding?

Beam search enhances the decoding process in sequence-to-sequence models compared to greedy decoding by considering multiple candidate sequences at each step, rather than just the single most likely word. Greedy decoding selects the word with the highest probability at each step, without considering the potential impact on future words. This can lead to suboptimal sequences, as a locally optimal choice might not lead to the best overall sequence. Beam search, on the other hand, maintains a 'beam' of 'k' candidate sequences, where 'k' is the beam width. At each step, beam search expands each of the 'k' candidate sequences by considering the top 'k' most likely words. It then calculates the probability of each resulting sequence and selects the top 'k' sequences to keep in the beam. This process is repeated until the end-of-sequence token is generated or a maximum sequence length is reached. By considering multiple candidate sequences, beam search is more likely to find a better overall sequence compared to greedy decoding. For example, in machine translation, greedy decoding might translate a sentence word by word, making locally optimal choices that lead to a grammatically incorrect or semantically nonsensical translation. Beam search, by considering multiple possible translations at each step, is more likely to find a fluent and accurate translation. Therefore, beam search offers a more robust decoding process compared to greedy decoding by exploring a wider range of possible sequences.