Explain the concept of beam search and its role in improving the quality of generated translations.

Beam search is a heuristic search algorithm used during inference to improve the quality of generated translations by exploring multiple possible output sequences at each step of the decoding process. Instead of greedily selecting the most likely word at each step, beam search maintains a "beam" of B candidate sequences, where B is the beam size. At each step, the model generates probabilities for all possible words in the vocabulary. For each of the B candidate sequences in the beam, beam search considers the top K most likely words and extends the candidate sequence with each of these words, creating B K new candidate sequences. It then scores these new sequences and selects the top B sequences to form the new beam for the next step. The scoring function typically combines the log probabilities of the generated words with a length penalty to encourage the model to generate longer and more complete translations. This process is repeated until the model generates an end-of-sentence token or reaches a maximum sequence length. The final translation is selected as the highest-scoring sequence in the beam. Beam search improves the quality of generated translations by exploring a larger space of possible output sequences compared to greedy decoding. Greedy decoding always selects the most likely word at each step, which can lead to suboptimal translations if the most likely word at an early stage turns out to be a poor choice later on. Beam search allows the model to consider multiple possibilities and to recover from early mistakes, leading to more accurate and fluent translations. The beam size B controls the trade-off between translation quality and computational cost. Larger beam sizes lead to better translations but also require more computation. Therefore, the beam size is typically chosen based on the specific task and the available computational resources.