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The growing popularity of generative language models has amplified interestin interactive methods to guide model outputs. Prompt refinement is consideredone of the most effective means to influence output among these methods. Weidentify several challenges associated with prompting large language models,categorized into data- and model-specific, linguistic, and socio-linguisticchallenges. A comprehensive examination of model outputs, including runner-upcandidates and their corresponding probabilities, is needed to address theseissues. The beam search tree, the prevalent algorithm to sample model outputs,can inherently supply this information. Consequently, we introduce aninteractive visual method for investigating the beam search tree, facilitatinganalysis of the decisions made by the model during generation. Wequantitatively show the value of exposing the beam search tree and present fivedetailed analysis scenarios addressing the identified challenges. Ourmethodology validates existing results and offers additional insights.

Revealing the Unwritten: Visual Investigation of Beam Search Trees to Address Language Model Prompting Challenges