3 papers
cs.IR2026
Formalized Information Needs Improve Large-Language-Model Relevance Judgments
Jüri Keller, Maik Fröbe, Björn Engelmann +4
Cranfield-style retrieval evaluations with too few or too many relevant documents or with low inter-assessor agreement on relevance can reduce the reliability of observations. In e…
cs.IR2025
REANIMATOR: Reanimate Retrieval Test Collections with Extracted and Synthetic Resources
Björn Engelmann, Fabian Haak, Philipp Schaer +3
Retrieval test collections are essential for evaluating information retrieval systems, yet they often lack generalizability across tasks. To overcome this limitation, we introduce…
cs.IR2024
Investigating Bias in Political Search Query Suggestions by Relative Comparison with LLMs
Fabian Haak, Björn Engelmann, Christin Katharina Kreutz +1
Search query suggestions affect users' interactions with search engines, which then influences the information they encounter. Thus, bias in search query suggestions can lead to ex…