@inproceedings{spinner-etal-2025-revealing,
    title = "Revealing the Unwritten: Visual Investigation of Beam Search Trees to Address Language Model Prompting Challenges",
    author = {Spinner, Thilo  and
      Sevastjanova, Rita  and
      Kehlbeck, Rebecca  and
      St{\"a}hle, Tobias  and
      Keim, Daniel A.  and
      Deussen, Oliver  and
      Spitz, Andreas  and
      El-Assady, Mennatallah},
    editor = "Mishra, Pushkar  and
      Muresan, Smaranda  and
      Yu, Tao",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-demo.29/",
    pages = "295--306",
    ISBN = "979-8-89176-253-4",
    abstract = "The present popularity of generative language models has amplified interest in interactive methods to guide model outputs. Prompt refinement is considered one of the most effective means to influence output among these methods. We identify several challenges associated with prompting large language models, categorized into data- and model-specific, linguistic, and socio-linguistic challenges. A comprehensive examination of model outputs, including runner-up candidates and their corresponding probabilities, is needed to address these issues. The beam search tree, the prevalent algorithm to sample model outputs, can inherently supply this information. Consequently, we leverage an interactive visual method for investigating the beam search tree, facilitating analysis of the decisions made by the model during generation. Our explorative approach validates existing results and offers additional insights."
}