9 citations · 16 across the 9 of their papers we have counts for
12 papers
Agent4RE: A Self-Refining Multi-agent Framework for End-to-End Software Requirements Engineering and Benchmarking
Yongjian Tang, Linhan Li, Thomas Runkler
Existing LLM-based approaches for software Requirements Engineering (RE) typically rely on basic prompting strategies or rudimentary agent collaboration, under-utilizing the full p…
Ontology-Grounded, Reasoner-Verified Benchmarks for Evaluating LLM Reasoning in Scientific AI
Nishtha N. Vaidya, Stephan Grimm, Thomas Hubauer +1
Large language models (LLMs) increasingly underpin scientific AI applications that reason over structured knowledge, from biomedical question answering to materials informatics. Ho…
Also Small Models Can Reasonably Self-Evaluate Their Confidence
Idil Kapikiran, Thomas Decker, Thomas Runkler
This study systematically evaluates self-evaluation-based uncertainty quantification across different language models of varying sizes on question-answering tasks spanning general…
Evaluating Federated Pre-Training: On the Reliability of Downstream Fine-Tuning and Intrinsic Evaluation
Claudia Grosser, Maike Heuer, Denis Krompass +1
Federated pre-training offers a way to train foundation models on private or distributed data without centralizing the underlying datasets. However, evaluating federated pre-traini…
Agent4cs: A Multi-agent System for Code Summarization in Large Hierarchical Codebases
Yongjian Tang, Ezgi Sarikayak, Doruk Tuncel +2
Understanding large, complex codebases, especially those with obfuscated structures and incomplete documentation, remains a significant challenge. Existing code summarization solut…
LLM-Based Agentic Systems for Software Engineering: Challenges and Opportunities
Yongjian Tang, Thomas Runkler
Despite recent advancements in Large Language Models (LLMs), complex Software Engineering (SE) tasks require more collaborative and specialized approaches. This concept paper syste…
The Few-shot Dilemma: Over-prompting Large Language Models
Yongjian Tang, Doruk Tuncel, Christian Koerner +1
Over-prompting, a phenomenon where excessive examples in prompts lead to diminished performance in Large Language Models (LLMs), challenges the conventional wisdom about in-context…
Is Q-learning an Ill-posed Problem?
Philipp Wissmann, Daniel Hein, Steffen Udluft +1
This paper investigates the instability of Q-learning in continuous environments, a challenge frequently encountered by practitioners. Traditionally, this instability is attributed…
Conceptual In-Context Learning and Chain of Concepts: Solving Complex Conceptual Problems Using Large Language Models
Nishtha N. Vaidya, Thomas Runkler, Thomas Hubauer +2
Science and engineering problems fall in the category of complex conceptual problems that require specific conceptual information (CI) like math/logic -related know-how, process in…
FsPONER: Few-shot Prompt Optimization for Named Entity Recognition in Domain-specific Scenarios
Yongjian Tang, Rakebul Hasan, Thomas Runkler
Large Language Models (LLMs) have provided a new pathway for Named Entity Recognition (NER) tasks. Compared with fine-tuning, LLM-powered prompting methods avoid the need for train…
Fusion of Domain-Adapted Vision and Language Models for Medical Visual Question Answering
Cuong Nhat Ha, Shima Asaadi, Sanjeev Kumar Karn +3
Vision-language models, while effective in general domains and showing strong performance in diverse multi-modal applications like visual question-answering (VQA), struggle to main…
Wiki-TabNER: Integrating Named Entity Recognition into Wikipedia Tables
Aneta Koleva, Martin Ringsquandl, Ahmed Hatem +2
Interest in solving table interpretation tasks has grown over the years, yet it still relies on existing datasets that may be overly simplified. This is potentially reducing the ef…