Beam search represents a popular decoding strategy employed in language generation tasks, such as text generation and machine translation. At its core, beam search operates by iteratively expanding a set of candidate sequences, known as the beam, and selecting the most promising candidates based on a predefined scoring criterion. By exploring multiple potential sequences in parallel and retaining a fixed number of top candidates (the beam width), beam search enables language models to generate coherent and contextually relevant text while balancing between exploration and exploitation.
