I am a fourth year PhD Candidate at Brown University working with Carsten Eickhoff in the Health NLP Lab. My research centers on explainable information retrieval (XIR), particularly developing methods for interpretable and explainable search. I am also broadly interested in the interpretability of LLMs and the applications of ML/NLP/IR to the biomedical domain. Overall, my work aims to bridge the gap between model transparency and practical deployment, contributing to the development of more accountable and trustworthy AI systems.
Previously, I worked as a full-stack software engineer for FreeWheel in NYC to develop advertising technology solutions. I received my BA in Computer Science and French from Wellesley College, where I was also a member of the Track & Field and Swimming & Diving Teams.
In my spare time, I enjoy running, hiking, cooking, and exploring local restaurants/bars.
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Brown University-
Ph.D. in Computer ScienceSep. 2021 - Present
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Sc.M. in Computer ScienceSep. 2021 - May 2023
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Wellesley College-
B.A. in Computer Science and FrenchSep. 2015 - May 2019
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Research Interests
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Interpretability/Explainability
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Information Retrieval
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Natural Language Processing
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Biomedical Applications of ML
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Selected Publications (view all )
Axiomatic Causal Interventions for Reverse Engineering Relevance Computation in Neural Retrieval Models
Catherine Chen, Jack Merullo, Carsten Eickhoff
SIGIR 2024
Neural models have demonstrated remarkable performance across diverse ranking tasks. However, the processes and internal mechanisms along which they determine relevance are still largely unknown. Existing approaches for analyzing neural ranker behavior with respect to IR properties rely either on assessing overall model behavior or employing probing methods that may offer an incomplete understanding of causal mechanisms. To provide a more granular understanding of internal model decision-making processes, we propose the use of causal interventions to reverse engineer neural rankers, and demonstrate how mechanistic interpretability methods can be used to isolate components satisfying term-frequency axioms within a ranking model. We identify a group of attention heads that detect duplicate tokens in earlier layers of the model, then communicate with downstream heads to compute overall document relevance. More generally, we propose that this style of mechanistic analysis opens up avenues for reverse engineering the processes neural retrieval models use to compute relevance. This work aims to initiate granular interpretability efforts that will not only benefit retrieval model development and training, but ultimately ensure safer deployment of these models.
Evaluating Search System Explainability with Psychometrics and Crowdsourcing
Catherine Chen, Carsten Eickhoff
SIGIR 2024
Information retrieval (IR) systems have become an integral part of our everyday lives. As search engines, recommender systems, and conversational agents are employed across various domains from recreational search to clinical decision support, there is an increasing need for transparent and explainable systems to guarantee accountable, fair, and unbiased results. Despite many recent advances towards explainable AI and IR techniques, there is no consensus on what it means for a system to be explainable. Although a growing body of literature suggests that explainability is comprised of multiple subfactors, virtually all existing approaches treat it as a singular notion. In this paper, we examine explainability in Web search systems, leveraging psychometrics and crowdsourcing to identify human-centered factors of explainability.
Outlier Dimensions Encode Task Specific Knowledge
William Rudman, Catherine Chen, Carsten Eickhoff
EMNLP 2023
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 ablating these outlier dimensions in LLM representations hurts downstream performance, outlier dimensions are detrimental to the representational quality of embeddings. In this study, we investigate how fine-tuning impacts outlier dimensions and show that 1) outlier dimensions that occur in pre-training persist in fine-tuned models and 2) a single outlier dimension can complete downstream tasks with a minimal error rate. Our results suggest that outlier dimensions can encode crucial task-specific knowledge and that the value of a representation in a single outlier dimension drives downstream model decisions.