1 citations · 3 across the 7 of their papers we have counts for
7 papers
A Mechanistic Analysis of Gender Sensitivity in Dense Retrieval Models
Catherine Chen, Maarten de Rijke, Carsten Eickhoff
While gender bias in dense retrieval models is well documented, with prior work showing that models often score male-gendered documents higher than female or neutral variants, the…
Pathway to Relevance: How Cross-Encoders Implement a Semantic Variant of BM25
Meng Lu, Catherine Chen, Carsten Eickhoff
Mechanistic interpretation has greatly contributed to a more detailed understanding of generative language models, enabling significant progress in identifying structures that impl…
MechIR: A Mechanistic Interpretability Framework for Information Retrieval
Andrew Parry, Catherine Chen, Carsten Eickhoff +1
Mechanistic interpretability is an emerging diagnostic approach for neural models that has gained traction in broader natural language processing domains. This paradigm aims to pro…
Axiomatic Causal Interventions for Reverse Engineering Relevance Computation in Neural Retrieval Models
Catherine Chen, Jack Merullo, Carsten Eickhoff
Neural models have demonstrated remarkable performance across diverse ranking tasks. However, the processes and internal mechanisms along which they determine relevance are still l…
Outlier Dimensions Encode Task-Specific Knowledge
William Rudman, Catherine Chen, Carsten Eickhoff
Representations from large language models (LLMs) are known to be dominated by a small subset of dimensions with exceedingly high variance. Previous works have argued that although…
SSE: A Metric for Evaluating Search System Explainability
Catherine Chen, Carsten Eickhoff
Explainable Information Retrieval (XIR) is a growing research area focused on enhancing transparency and trustworthiness of the complex decision-making processes taking place in mo…
Evaluating Search System Explainability with Psychometrics and Crowdsourcing
Catherine Chen, Carsten Eickhoff
As information retrieval (IR) systems, such as search engines and conversational agents, become ubiquitous in various domains, the need for transparent and explainable systems grow…