4 citations · 4 across the 2 of their papers we have counts for
2 papers
cs.HC2024★ 4 cited
generAItor: Tree-in-the-Loop Text Generation for Language Model Explainability and Adaptation
Thilo Spinner, Rebecca Kehlbeck, Rita Sevastjanova +4
Large language models (LLMs) are widely deployed in various downstream tasks, e.g., auto-completion, aided writing, or chat-based text generation. However, the considered output ca…
cs.CL2023
Revealing the Unwritten: Visual Investigation of Beam Search Trees to Address Language Model Prompting Challenges
Thilo Spinner, Rebecca Kehlbeck, Rita Sevastjanova +5
The growing popularity of generative language models has amplified interest in interactive methods to guide model outputs. Prompt refinement is considered one of the most effective…