12 citations · 33 across the 25 of their papers we have counts for
28 papers
Prompting Is All You Need: Multi-view Prompting Large Language Models for Aspect-Based Sentiment Analysis
Nils Constantin Hellwig, Niklas Donhauser, Jakob Fehle +2
Recent work explored the capabilities of Large Language Models (LLMs) in Aspect-Based Sentiment Analysis (ABSA) through few-shot prompting, requiring substantially fewer annotated…
Annotation Quality in Aspect-Based Sentiment Analysis: A Case Study Comparing Experts, Students, Crowdworkers, and Large Language Model
Niklas Donhauser, Jakob Fehle, Nils Constantin Hellwig +3
Aspect-Based Sentiment Analysis (ABSA) enables fine-grained opinion analysis by identifying sentiments toward specific aspects or targets within a text. While ABSA has been widely…
Zero-Shot to Full-Resource: Cross-lingual Transfer Strategies for Aspect-Based Sentiment Analysis
Jakob Fehle, Nils Constantin Hellwig, Udo Kruschwitz +1
Aspect-based Sentiment Analysis (ABSA) extracts fine-grained opinions toward specific aspects within text but remains largely English-focused despite major advances in transformer-…
From Questions to Trust Reports: A LLM-IR Framework for the TREC 2025 DRAGUN Track
Ignacy Alwasiak, Kene Nnolim, Jaclyn Thi +5
The DRAGUN Track at TREC 2025 targets the growing need for effective support tools that help users evaluate the trustworthiness of online news. We describe the UR_Trecking system s…
nchellwig at SemEval-2026 Task 3: Self-Consistent Structured Generation (SCSG) for Dimensional Aspect-Based Sentiment Analysis using Large Language Models
Nils Constantin Hellwig, Jakob Fehle, Udo Kruschwitz +1
We present Self-Consistent Structured Generation (SCSG) for Dimensional Aspect-Based Sentiment Analysis in SemEval-2026 Task 3 (Track A). SCSG enhances prediction reliability by ex…
LLM-as-an-Annotator: Training Lightweight Models with LLM-Annotated Examples for Aspect Sentiment Tuple Prediction
Nils Constantin Hellwig, Jakob Fehle, Udo Kruschwitz +1
Training models for Aspect-Based Sentiment Analysis (ABSA) tasks requires manually annotated data, which is expensive and time-consuming to obtain. This paper introduces LA-ABSA, a…
AnnoABSA: A Web-Based Annotation Tool for Aspect-Based Sentiment Analysis with Retrieval-Augmented Suggestions
Nils Constantin Hellwig, Jakob Fehle, Udo Kruschwitz +1
We introduce AnnoABSA, the first web-based annotation tool to support the full spectrum of Aspect-Based Sentiment Analysis (ABSA) tasks. The tool is highly customizable, enabling f…
Seek and You Shall Find: Design & Evaluation of a Context-Aware Interactive Search Companion
Markus Bink, Marten Risius, Udo Kruschwitz +1
Many users struggle with effective online search and critical evaluation, especially in high-stakes domains like health, while often overestimating their digital literacy. Thus, in…
"Can You Tell Me?": Designing Copilots to Support Human Judgement in Online Information Seeking
Markus Bink, Marten Risius, Udo Kruschwitz +1
Generative AI (GenAI) tools are transforming information seeking, but their fluent, authoritative responses risk overreliance and discourage independent verification and reasoning.…
MedNuggetizer: Confidence-Based Information Nugget Extraction from Medical Documents
Gregor Donabauer, Samy Ateia, Udo Kruschwitz +6
We present MedNuggetizer, https://mednugget-ai.de/; access is available upon request.}, a tool for query-driven extraction and clustering of information nuggets from medical docume…
LLM-Based Information Extraction to Support Scientific Literature Research and Publication Workflows
Samy Ateia, Udo Kruschwitz, Melanie Scholz +2
The increasing volume of scholarly publications requires advanced tools for efficient knowledge discovery and management. This paper introduces ongoing work on a system using Large…
Can Language Models Critique Themselves? Investigating Self-Feedback for Retrieval Augmented Generation at BioASQ 2025
Samy Ateia, Udo Kruschwitz
Agentic Retrieval Augmented Generation (RAG) and 'deep research' systems aim to enable autonomous search processes where Large Language Models (LLMs) iteratively refine outputs. Ho…
Why am I seeing this? Towards recognizing social media recommender systems with missing recommendations
Sabrina Guidotti, Sabrina Patania, Giuseppe Vizzari +4
Social media plays a crucial role in shaping society, often amplifying polarization and spreading misinformation. These effects stem from complex dynamics involving user interactio…
A Reproducibility Study of Graph-Based Legal Case Retrieval
Gregor Donabauer, Udo Kruschwitz
Legal retrieval is a widely studied area in Information Retrieval (IR) and a key task in this domain is retrieving relevant cases based on a given query case, often done by applyin…
Query Smarter, Trust Better? Exploring Search Behaviours for Verifying News Accuracy
David Elsweiler, Samy Ateia, Markus Bink +8
While it is often assumed that searching for information to evaluate misinformation will help identify false claims, recent work suggests that search behaviours can instead reinfor…
Understanding Learner-LLM Chatbot Interactions and the Impact of Prompting Guidelines
Cansu Koyuturk, Emily Theophilou, Sabrina Patania +13
Large Language Models (LLMs) have transformed human-computer interaction by enabling natural language-based communication with AI-powered chatbots. These models are designed to be…
Use Me Wisely: AI-Driven Assessment for LLM Prompting Skills Development
Dimitri Ognibene, Gregor Donabauer, Emily Theophilou +10
The use of large language model (LLM)-powered chatbots, such as ChatGPT, has become popular across various domains, supporting a range of tasks and processes. However, due to the i…
Do we still need Human Annotators? Prompting Large Language Models for Aspect Sentiment Quad Prediction
Nils Constantin Hellwig, Jakob Fehle, Udo Kruschwitz +1
Aspect sentiment quad prediction (ASQP) facilitates a detailed understanding of opinions expressed in a text by identifying the opinion term, aspect term, aspect category and senti…
Token-Level Graphs for Short Text Classification
Gregor Donabauer, Udo Kruschwitz
The classification of short texts is a common subtask in Information Retrieval (IR). Recent advances in graph machine learning have led to interest in graph-based approaches for lo…
BioRAGent: A Retrieval-Augmented Generation System for Showcasing Generative Query Expansion and Domain-Specific Search for Scientific Q&A
Samy Ateia, Udo Kruschwitz
We present BioRAGent, an interactive web-based retrieval-augmented generation (RAG) system for biomedical question answering. The system uses large language models (LLMs) for query…
The AI Interface: Designing for the Ideal Machine-Human Experience (Editorial)
Aparna Sundar, Tony Russell-Rose, Udo Kruschwitz +1
As artificial intelligence (AI) becomes increasingly embedded in daily life, designing intuitive, trustworthy, and emotionally resonant AI-human interfaces has emerged as a critica…
Modeling Social Media Recommendation Impacts Using Academic Networks: A Graph Neural Network Approach
Sabrina Guidotti, Gregor Donabauer, Simone Somazzi +3
The widespread use of social media has highlighted potential negative impacts on society and individuals, largely driven by recommendation algorithms that shape user behavior and s…
Can Open-Source LLMs Compete with Commercial Models? Exploring the Few-Shot Performance of Current GPT Models in Biomedical Tasks
Samy Ateia, Udo Kruschwitz
Commercial large language models (LLMs), like OpenAI's GPT-4 powering ChatGPT and Anthropic's Claude 3 Opus, have dominated natural language processing (NLP) benchmarks across diff…
Investigating Neural Machine Translation for Low-Resource Languages: Using Bavarian as a Case Study
Wan-Hua Her, Udo Kruschwitz
Machine Translation has made impressive progress in recent years offering close to human-level performance on many languages, but studies have primarily focused on high-resource la…