13 citations · 68 across the 49 of their papers we have counts for
17 papers · 1 filter
Big Tech-Funded AI Papers Have Higher Citation Impact, Greater Insularity, and Larger Recency Bias
Max Martin Gnewuch, Jan Philip Wahle, Terry Ruas +1
Over the past four decades, artificial intelligence (AI) research has flourished at the nexus of academia and industry. However, Big Tech companies have increasingly acquired the e…
Affect, Body, Cognition, Demographics, and Emotion: The ABCDE of Text Features for Computational Affective Science
Jan Philip Wahle, Krishnapriya Vishnubhotla, Bela Gipp +1
Work in Computational Affective Science and Computational Social Science explores a wide variety of research questions about people, emotions, behavior, and health. Such work often…
Challenging Tribal Knowledge -- Large Scale Measurement Campaign on Decentralized NAT Traversal
Dennis Trautwein, Cornelius Ihle, Moritz Schubotz +1
The promise of decentralized peer-to-peer (P2P) systems is fundamentally gated by the challenge of Network Address Translation (NAT) traversal, with existing solutions often reintr…
ALDEN: Reinforcement Learning for Active Navigation and Evidence Gathering in Long Documents
Tianyu Yang, Terry Ruas, Yijun Tian +3
Vision-language models (VLMs) excel at interpreting text-rich images but struggle with long, visually complex documents that demand analysis and integration of information spread a…
From AutoRecSys to AutoRecLab: A Call to Build, Evaluate, and Govern Autonomous Recommender-Systems Research Labs
Joeran Beel, Bela Gipp, Tobias Vente +2
Recommender-systems research has accelerated model and evaluation advances, yet largely neglects automating the research process itself. We argue for a shift from narrow AutoRecSys…
Overview of the Plagiarism Detection Task at PAN 2025
André Greiner-Petter, Maik Fröbe, Jan Philip Wahle +4
The generative plagiarism detection task at PAN 2025 aims at identifying automatically generated textual plagiarism in scientific articles and aligning them with their respective s…
Contrastive Learning Using Graph Embeddings for Domain Adaptation of Language Models in the Process Industry
Anastasia Zhukova, Jonas Lührs, Christian E. Lobmüller +1
Recent trends in NLP utilize knowledge graphs (KGs) to enhance pretrained language models by incorporating additional knowledge from the graph structures to learn domain-specific t…
Re-FRAME the Meeting Summarization SCOPE: Fact-Based Summarization and Personalization via Questions
Frederic Kirstein, Sonu Kumar, Terry Ruas +1
Meeting summarization with large language models (LLMs) remains error-prone, often producing outputs with hallucinations, omissions, and irrelevancies. We present FRAME, a modular…
MALLM: Multi-Agent Large Language Models Framework
Jonas Becker, Lars Benedikt Kaesberg, Niklas Bauer +3
Multi-agent debate (MAD) has demonstrated the ability to augment collective intelligence by scaling test-time compute and leveraging expertise. Current frameworks for multi-agent d…
Link Prediction for Event Logs in the Process Industry
Anastasia Zhukova, Thomas Walton, Christian E. Lobmüller +1
In the era of graph-based retrieval-augmented generation (RAG), link prediction is a significant preprocessing step for improving the quality of fragmented or incomplete domain-spe…
TrojanStego: Your Language Model Can Secretly Be A Steganographic Privacy Leaking Agent
Dominik Meier, Jan Philip Wahle, Paul Röttger +2
As large language models (LLMs) become integrated into sensitive workflows, concerns grow over their potential to leak confidential information. We propose TrojanStego, a novel thr…
SPaRC: A Spatial Pathfinding Reasoning Challenge
Lars Benedikt Kaesberg, Jan Philip Wahle, Terry Ruas +1
Existing reasoning datasets saturate and fail to test abstract, multi-step problems, especially pathfinding and complex rule constraint satisfaction. We introduce SPaRC (Spatial Pa…
Efficient Domain-adaptive Continual Pretraining for the Process Industry in the German Language
Anastasia Zhukova, Christian E. Matt, Bela Gipp
Domain-adaptive continual pretraining (DAPT) is a state-of-the-art technique that further trains a language model (LM) on its pretraining task, e.g., masked language modeling (MLM)…
Stay Focused: Problem Drift in Multi-Agent Debate
Jonas Becker, Lars Benedikt Kaesberg, Andreas Stephan +3
Multi-agent debate - multiple instances of large language models discussing problems in turn-based interaction - has shown promise for solving knowledge and reasoning tasks. Howeve…
Voting or Consensus? Decision-Making in Multi-Agent Debate
Lars Benedikt Kaesberg, Jonas Becker, Jan Philip Wahle +2
Much of the success of multi-agent debates depends on carefully choosing the right parameters. The decision-making protocol stands out as it can highly impact final model answers,…
You need to MIMIC to get FAME: Solving Meeting Transcript Scarcity with a Multi-Agent Conversations
Frederic Kirstein, Muneeb Khan, Jan Philip Wahle +2
Meeting summarization suffers from limited high-quality data, mainly due to privacy restrictions and expensive collection processes. We address this gap with FAME, a dataset of 500…
BRIGHTER: BRIdging the Gap in Human-Annotated Textual Emotion Recognition Datasets for 28 Languages
Shamsuddeen Hassan Muhammad, Nedjma Ousidhoum, Idris Abdulmumin +45
People worldwide use language in subtle and complex ways to express emotions. Although emotion recognition--an umbrella term for several NLP tasks--impacts various applications wit…