5 citations · 11 across the 4 of their papers we have counts for
4 papers
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…
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…
Visual Comparison of Language Model Adaptation
Rita Sevastjanova, Eren Cakmak, Shauli Ravfogel +2
Neural language models are widely used; however, their model parameters often need to be adapted to the specific domains and tasks of an application, which is time- and resource-co…
Beware the Rationalization Trap! When Language Model Explainability Diverges from our Mental Models of Language
Rita Sevastjanova, Mennatallah El-Assady
Language models learn and represent language differently than humans; they learn the form and not the meaning. Thus, to assess the success of language model explainability, we need…