21 citations · 52 across the 7 of their papers we have counts for
6 papers · 1 filter
How Do LLMs Cite? A Mechanistic Interpretation of Attribution in Retrieval-Augmented Generation
Ian van Dort, Maria Heuss
Retrieval-Augmented Generation (RAG) aims to enhance the trustworthiness of Large Language Models (LLMs) by grounding their outputs in external documents, often using inline citati…
Information Retrieval for Climate Impact
Maarten de Rijke, Bart van den Hurk, Flora Salim +28
The purpose of the MANILA24 Workshop on information retrieval for climate impact was to bring together researchers from academia, industry, governments, and NGOs to identify and di…
RankingSHAP -- Listwise Feature Attribution Explanations for Ranking Models
Maria Heuss, Maarten de Rijke, Avishek Anand
While SHAP (SHapley Additive exPlanations) and other feature attribution methods are commonly employed to explain model predictions, their application within information retrieval…
Predictive Uncertainty-based Bias Mitigation in Ranking
Maria Heuss, Daniel Cohen, Masoud Mansoury +2
Societal biases that are contained in retrieved documents have received increased interest. Such biases, which are often prevalent in the training data and learned by the model, ca…
Fairness of Exposure in Light of Incomplete Exposure Estimation
Maria Heuss, Fatemeh Sarvi, Maarten de Rijke
Fairness of exposure is a commonly used notion of fairness for ranking systems. It is based on the idea that all items or item groups should get exposure proportional to the merit…
Understanding and Mitigating the Effect of Outliers in Fair Ranking
Fatemeh Sarvi, Maria Heuss, Mohammad Aliannejadi +2
Traditional ranking systems are expected to sort items in the order of their relevance and thereby maximize their utility. In fair ranking, utility is complemented with fairness as…