5 citations · 8 across the 9 of their papers we have counts for
9 papers
A systematic Approach to constructing a Chance-and-Risk Matrix for Semiconductor Supply Chains
Ema Salkić, Alexander Fichtl, Philipp Ulrich +3
Semiconductor supply chains face escalating risks from geopolitical tensions, geographic concentration, and rapid technological shifts, yet no scalable system continuously extracts…
Safer Reasoning Traces: Measuring and Mitigating Chain-of-Thought Leakage in LLMs
Patrick Ahrend, Tobias Eder, Xiyang Yang +2
Chain-of-Thought (CoT) prompting improves LLM reasoning but can increase privacy risk by resurfacing personally identifiable information (PII) from the prompt into reasoning traces…
Agent2Agent Threats in Safety-Critical LLM Assistants: A Human-Centric Taxonomy
Lukas Stappen, Ahmet Erkan Turan, Johann Hagerer +1
The integration of Large Language Model (LLM)-based conversational agents into vehicles creates novel security challenges at the intersection of agentic AI, automotive safety, and…
Framework of Thoughts: A Foundation Framework for Dynamic and Optimized Reasoning based on Chains, Trees, and Graphs
Felix Fricke, Simon Malberg, Georg Groh
Prompting schemes such as Chain of Thought, Tree of Thoughts, and Graph of Thoughts can significantly enhance the reasoning capabilities of large language models. However, most exi…
Deep Research Agents Brings Deeper Harm
Shuo Chen, Zonggen Li, Xingyu Jin +8
We reveal that Deep Research (DR) agents systematically expose safety risks: simply submitting harmful queries that a standalone LLM would reject outright can elicit detailed and d…
Adapter-based Approaches to Knowledge-enhanced Language Models -- A Survey
Alexander Fichtl, Juraj Vladika, Georg Groh
Knowledge-enhanced language models (KELMs) have emerged as promising tools to bridge the gap between large-scale language models and domain-specific knowledge. KELMs can achieve hi…
Profiling Bias in LLMs: Stereotype Dimensions in Contextual Word Embeddings
Carolin M. Schuster, Maria-Alexandra Dinisor, Shashwat Ghatiwala +1
Large language models (LLMs) are the foundation of the current successes of artificial intelligence (AI), however, they are unavoidably biased. To effectively communicate the risks…
A Comprehensive Evaluation of Cognitive Biases in LLMs
Simon Malberg, Roman Poletukhin, Carolin M. Schuster +1
We present a large-scale evaluation of 30 cognitive biases in 20 state-of-the-art large language models (LLMs) under various decision-making scenarios. Our contributions include a…
Tuning Into Bias: A Computational Study of Gender Bias in Song Lyrics
Danqing Chen, Adithi Satish, Rasul Khanbayov +2
The application of text mining methods is becoming increasingly prevalent, particularly within Humanities and Computational Social Sciences, as well as in a broader range of discip…