128 citations · 523 across the 53 of their papers we have counts for
19 papers · 1 filter
AQA: Adaptive Question Answering in a Society of LLMs via Contextual Multi-Armed Bandit
Mohanna Hoveyda, Arjen P. de Vries, Maarten de Rijke +2
In question answering (QA), different questions can be effectively addressed with different answering strategies. Some require a simple lookup, while others need complex, multi-ste…
Real World Conversational Entity Linking Requires More Than Zeroshots
Mohanna Hoveyda, Arjen P. de Vries, Maarten de Rijke +1
Entity linking (EL) in conversations faces notable challenges in practical applications, primarily due to the scarcity of entity-annotated conversational datasets and sparse knowle…
Towards Empathetic Conversational Recommender Systems
Xiaoyu Zhang, Ruobing Xie, Yougang Lyu +7
Conversational recommender systems (CRSs) are able to elicit user preferences through multi-turn dialogues. They typically incorporate external knowledge and pre-trained language m…
Robust Neural Information Retrieval: An Adversarial and Out-of-distribution Perspective
Yu-An Liu, Ruqing Zhang, Jiafeng Guo +3
Recent advances in neural information retrieval (IR) models have significantly enhanced their effectiveness over various IR tasks. The robustness of these models, essential for ens…
Practical and Robust Safety Guarantees for Advanced Counterfactual Learning to Rank
Shashank Gupta, Harrie Oosterhuis, Maarten de Rijke
Counterfactual learning to rank (CLTR) can be risky and, in various circumstances, can produce sub-optimal models that hurt performance when deployed. Safe CLTR was introduced to m…
Benchmark Granularity and Model Robustness for Image-Text Retrieval
Mariya Hendriksen, Shuo Zhang, Ridho Reinanda +3
Image-Text Retrieval (ITR) systems are central to multimodal information access, with Vision-Language Models (VLMs) showing strong performance on standard benchmarks. However, thes…
Robust Information Retrieval
Yu-An Liu, Ruqing Zhang, Jiafeng Guo +1
Beyond effectiveness, the robustness of an information retrieval (IR) system is increasingly attracting attention. When deployed, a critical technology such as IR should not only d…
Estimating the Hessian Matrix of Ranking Objectives for Stochastic Learning to Rank with Gradient Boosted Trees
Jingwei Kang, Maarten de Rijke, Harrie Oosterhuis
Stochastic learning to rank (LTR) is a recent branch in the LTR field that concerns the optimization of probabilistic ranking models. Their probabilistic behavior enables certain r…
Are We Really Achieving Better Beyond-Accuracy Performance in Next Basket Recommendation?
Ming Li, Yuanna Liu, Sami Jullien +4
Next basket recommendation (NBR) is a special type of sequential recommendation that is increasingly receiving attention. So far, most NBR studies have focused on optimizing the ac…
Going Beyond Popularity and Positivity Bias: Correcting for Multifactorial Bias in Recommender Systems
Jin Huang, Harrie Oosterhuis, Masoud Mansoury +2
Two typical forms of bias in user interaction data with recommender systems (RSs) are popularity bias and positivity bias, which manifest themselves as the over-representation of i…
Rethinking the Evaluation of Dialogue Systems: Effects of User Feedback on Crowdworkers and LLMs
Clemencia Siro, Mohammad Aliannejadi, Maarten de Rijke
In ad-hoc retrieval, evaluation relies heavily on user actions, including implicit feedback. In a conversational setting such signals are usually unavailable due to the nature of t…
Ranked List Truncation for Large Language Model-based Re-Ranking
Chuan Meng, Negar Arabzadeh, Arian Askari +2
We study ranked list truncation (RLT) from a novel "retrieve-then-re-rank" perspective, where we optimize re-ranking by truncating the retrieved list (i.e., trim re-ranking candida…
Context Does Matter: Implications for Crowdsourced Evaluation Labels in Task-Oriented Dialogue Systems
Clemencia Siro, Mohammad Aliannejadi, Maarten de Rijke
Crowdsourced labels play a crucial role in evaluating task-oriented dialogue systems (TDSs). Obtaining high-quality and consistent ground-truth labels from annotators presents chal…
Large Language Models for Next Point-of-Interest Recommendation
Peibo Li, Maarten de Rijke, Hao Xue +3
The next Point of Interest (POI) recommendation task is to predict users' immediate next POI visit given their historical data. Location-Based Social Network (LBSN) data, which is…
Query Performance Prediction using Relevance Judgments Generated by Large Language Models
Chuan Meng, Negar Arabzadeh, Arian Askari +2
Query performance prediction (QPP) aims to estimate the retrieval quality of a search system for a query without human relevance judgments. Previous QPP methods typically return a…
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…
Multimodal Learned Sparse Retrieval with Probabilistic Expansion Control
Thong Nguyen, Mariya Hendriksen, Andrew Yates +1
Learned sparse retrieval (LSR) is a family of neural methods that encode queries and documents into sparse lexical vectors that can be indexed and retrieved efficiently with an inv…
CorpusBrain++: A Continual Generative Pre-Training Framework for Knowledge-Intensive Language Tasks
Jiafeng Guo, Changjiang Zhou, Ruqing Zhang +4
Knowledge-intensive language tasks (KILTs) typically require retrieving relevant documents from trustworthy corpora, e.g., Wikipedia, to produce specific answers. Very recently, a…
Demonstrating and Reducing Shortcuts in Vision-Language Representation Learning
Maurits Bleeker, Mariya Hendriksen, Andrew Yates +1
Vision-language models (VLMs) mainly rely on contrastive training to learn general-purpose representations of images and captions. We focus on the situation when one image is assoc…