33 citations · 61 across the 42 of their papers we have counts for
11 papers · 1 filter
A Design Space for Intelligent Agents in Mixed-Initiative Visual Analytics
Tobias Stähle, Matthijs Jansen op de Haar, Sophia Boyer +3
Mixed-initiative visual analytics (VA) systems, where human and artificial intelligence (AI) agents collaborate as equal partners during analysis, represented a paradigm shift in h…
SemanticTours: A Conceptual Framework for Non-Linear, Knowledge Graph-Driven Data Tours
Daniel Fürst, Matthijs Jansen op de Haar, Mennatallah El-Assady +2
Interactive tours help users explore datasets and provide onboarding. They rely on a linear sequence of views, showing a curated set of relevant data selections and introduce user…
Dia-Lingle: A Gamified Interface for Dialectal Data Collection
Jiugeng Sun, Rita Sevastjanova, Sina Ahmadi +2
Dialects suffer from the scarcity of computational textual resources as they exist predominantly in spoken rather than written form and exhibit remarkable geographical diversity. C…
CafGa: Customizing Feature Attributions to Explain Language Models
Alan Boyle, Furui Cheng, Vilém Zouhar +1
Feature attribution methods, such as SHAP and LIME, explain machine learning model predictions by quantifying the influence of each input component. When applying feature attributi…
Beyond Quantification: Navigating Uncertainty in Professional AI Systems
Sylvie Delacroix, Diana Robinson, Umang Bhatt +12
The growing integration of large language models across professional domains transforms how experts make critical decisions in healthcare, education, and law. While significant res…
DxHF: Providing High-Quality Human Feedback for LLM Alignment via Interactive Decomposition
Danqing Shi, Furui Cheng, Tino Weinkauf +2
Human preferences are widely used to align large language models (LLMs) through methods such as reinforcement learning from human feedback (RLHF). However, the current user interfa…
Explainable Mapper: Charting LLM Embedding Spaces Using Perturbation-Based Explanation and Verification Agents
Xinyuan Yan, Rita Sevastjanova, Sinie van der Ben +2
Large language models (LLMs) produce high-dimensional embeddings that capture rich semantic and syntactic relationships between words, sentences, and concepts. Investigating the to…
Co-DETECT: Collaborative Discovery of Edge Cases in Text Classification
Chenfei Xiong, Jingwei Ni, Yu Fan +10
We introduce Co-DETECT (Collaborative Discovery of Edge cases in TExt ClassificaTion), a novel mixed-initiative annotation framework that integrates human expertise with automatic…
Concept-Level Explainability for Auditing & Steering LLM Responses
Kenza Amara, Rita Sevastjanova, Mennatallah El-Assady
As large language models (LLMs) become widely deployed, concerns about their safety and alignment grow. An approach to steer LLM behavior, such as mitigating biases or defending ag…
LayerFlow: Layer-wise Exploration of LLM Embeddings using Uncertainty-aware Interlinked Projections
Rita Sevastjanova, Robin Gerling, Thilo Spinner +1
Large language models (LLMs) represent words through contextual word embeddings encoding different language properties like semantics and syntax. Understanding these properties is…
Reward Learning from Multiple Feedback Types
Yannick Metz, András Geiszl, Raphaël Baur +1
Learning rewards from preference feedback has become an important tool in the alignment of agentic models. Preference-based feedback, often implemented as a binary comparison betwe…