👉 Beam search is a popular algorithm used in natural language processing (NLP) for generating sequences of text, such as sentences or phrases. It works by exploring multiple potential paths through a sequence of decisions, much like a beam of light spreading out from a source. At each step, the algorithm predicts the most likely next word based on the current context and the probabilities of all possible next words, effectively considering a "beam" of the most probable sequences. This process is repeated iteratively, expanding the beam width over time to include more diverse but less likely sequences. While beam search helps balance between generating coherent and varied text, it can be computationally expensive due to the need to evaluate many potential sequences. To mitigate this, techniques like beam search with limited memory or using a fixed beam width are often employed to find a practical trade-off between performance and efficiency.
