3 papers
cs.IR2025
Reproducing and Extending Causal Insights Into Term Frequency Computation in Neural Rankers
Cile van Marken, Roxana Petcu
Neural ranking models have shown outstanding performance across a variety of tasks, such as document retrieval, re-ranking, question answering and conversational retrieval. However…
cs.IR2025
Interpreting Multilingual and Document-Length Sensitive Relevance Computations in Neural Retrieval Models through Axiomatic Causal Interventions
Oliver Savolainen, Dur e Najaf Amjad, Roxana Petcu
This reproducibility study analyzes and extends the paper "Axiomatic Causal Interventions for Reverse Engineering Relevance Computation in Neural Retrieval Models," which investiga…
cs.IR2025
Beyond Reproducibility: Advancing Zero-shot LLM Reranking Efficiency with Setwise Insertion
Jakub Podolak, Leon Peric, Mina Janicijevic +1
This study presents a comprehensive reproducibility and extension analysis of the Setwise prompting methodology for zero-shot ranking with Large Language Models (LLMs), as proposed…