7 papers
Faithful or Findable? Evaluating LLM-Generated Metadata for RDF Dataset Search
Riccardo Terrenzi, Serkan Ayvaz
Dataset search depends heavily on metadata, making LLM-generated metadata a consequential form of synthetic content in retrieval systems. We study six metadata-generation settings…
PIPER: Content-Based Table Search via profiling and LLM-Generated Pseudoqueries
Riccardo Terrenzi, Matteo Falconi, Serkan Ayvaz +1
The rapid growth of tabular datasets in data lakes, data spaces, and open data portals makes effective dataset search essential for reuse and analysis. Existing search systems rely…
Why Neighborhoods Matter: Traversal Context and Provenance in Agentic GraphRAG
Riccardo Terrenzi, Maximilian von Zastrow, Serkan Ayvaz
Retrieval-Augmented Generation can improve factuality by grounding answers in external evidence, but Agentic GraphRAG complicates what it means for citations to be faithful. In the…
The Open-Box Fallacy: Why AI Deployment Needs a Calibrated Verification Regime
Phongsakon Mark Konrad, Tim Lukas Adam, Ane Cathrine Holst Merrild +4
AI deployment in sensitive domains such as health care, credit, employment, and criminal justice is often treated as unsafe to authorize until model internals can be explained. Thi…
CAKE: Cloud Architecture Knowledge Evaluation of Large Language Models
Tim Lukas Adam, Phongsakon Mark Konrad, Riccardo Terrenzi +4
In today's software architecture, large language models (LLMs) serve as software architecture co-pilots. However, no benchmark currently exists to evaluate large language models' a…
Architecture Without Architects: How AI Coding Agents Shape Software Architecture
Phongsakon Mark Konrad, Tim Lukas Adam, Riccardo Terrenzi +1
AI coding agents select frameworks, scaffold infrastructure, and wire integrations, often in seconds. These are architectural decisions, yet almost no one reviews them as such. We…