@inproceedings{shaham-levy-2022-get,
    title = "What Do You Get When You Cross Beam Search with Nucleus Sampling?",
    author = "Shaham, Uri  and
      Levy, Omer",
    editor = "Tafreshi, Shabnam  and
      Sedoc, Jo{\~a}o  and
      Rogers, Anna  and
      Drozd, Aleksandr  and
      Rumshisky, Anna  and
      Akula, Arjun",
    booktitle = "Proceedings of the Third Workshop on Insights from Negative Results in NLP",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2022.insights-1.5/",
    doi = "10.18653/v1/2022.insights-1.5",
    pages = "38--45",
    abstract = "We combine beam search with the probabilistic pruning technique of nucleus sampling to create two deterministic nucleus search algorithms for natural language generation. The first algorithm, p-exact search, locally prunes the next-token distribution and performs an exact search over the remaining space. The second algorithm, dynamic beam search, shrinks and expands the beam size according to the entropy of the candidate{'}s probability distribution. Despite the probabilistic intuition behind nucleus search, experiments on machine translation and summarization benchmarks show that both algorithms reach the same performance levels as standard beam search."
}