17 papers
When Attention Goes Blind: Numerical Failure in ALiBi Positional Encodings
Christopher Schröder, Christopher Schröder, Lukas Gienapp +3
We identify a previously overlooked failure mode of ALiBi positional encoding: its linear bias scaling underflows floating-point precision, which zeroes out a large fraction of att…
Data Annotations as Pedagogical Hints: From Subjective Labels to Critical Thinking
Ralf Raumanns, Theresa Elstner, Louis Ferger-Andrews +5
Machine learning courses often use pre-labeled datasets, hiding the subjectivity of human annotation. This creates students with an overly trusting view of AI data and models, unde…
Detecting RAG Advertisements Across Advertising Styles
Sebastian Heineking, Wilhelm Pertsch, Ines Zelch +4
Large language models (LLMs) enable a new form of advertising for retrieval-augmented generation (RAG) systems in which organic responses are blended with contextually relevant ads…
Representation Fidelity:Auditing Algorithmic Decisions About Humans Using Self-Descriptions
Theresa Elstner, Martin Potthast
This paper introduces a new dimension for validating algorithmic decisions about humans by measuring the fidelity of their representations. Representation Fidelity measures if deci…
Overview of PAN 2026: Voight-Kampff Generative AI Detection, Text Watermarking, Multi-Author Writing Style Analysis, Generative Plagiarism Detection, and Reasoning Trajectory Detection
Janek Bevendorff, Maik Fröbe, André Greiner-Petter +9
The goal of the PAN workshop is to advance computational stylometry and text forensics via objective and reproducible evaluation. In 2026, we run the following five tasks: (1) Voig…
Topic-Specific Classifiers are Better Relevance Judges than Prompted LLMs
Lukas Gienapp, Martin Potthast, Andrew Yates +2
The unjudged document problem, where systems that did not contribute to the original judgement pool may retrieve documents without a relevance judgement, is a key obstacle to the r…