activity
20192025
most citedgenerAItor: Tree-in-the-Loop Text Generation for Language Model Explainability and Adaptation

4 citations · 4 across the 3 of their papers we have counts for

collaborators

6 papers

cs.GR2025

Neighborhood-Preserving Voronoi Treemaps

Patrick Paetzold, Rebecca Kehlbeck, Yumeng Xue +3

Voronoi treemaps are used to depict nodes and their hierarchical relationships simultaneously. However, in addition to the hierarchical structure, data attributes, such as co-occur…

cs.HC20244 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…

cs.DS2021

SpEuler: Semantics-preserving Euler Diagrams

Rebecca Kehlbeck, Jochen Görtler, Yunhai Wang +1

Creating comprehensible visualizations of highly overlapping set-typed data is a challenging task due to its complexity. To facilitate insights into set connectivity and to leverag…

cs.HC2019

Semantic Concept Spaces: Guided Topic Model Refinement using Word-Embedding Projections

Mennatallah El-Assady, Rebecca Kehlbeck, Christopher Collins +2

We present a framework that allows users to incorporate the semantics of their domain knowledge for topic model refinement while remaining model-agnostic. Our approach enables user…

cs.CL2019

VIANA: Visual Interactive Annotation of Argumentation

Fabian Sperrle, Rita Sevastjanova, Rebecca Kehlbeck +1

Argumentation Mining addresses the challenging tasks of identifying boundaries of argumentative text fragments and extracting their relationships. Fully automated solutions do not…