22 citations · 65 across the 12 of their papers we have counts for
12 papers
RMIT-ADM+S at the MMU-RAG NeurIPS 2025 Competition
Kun Ran, Marwah Alaofi, Danula Hettiachchi +9
This paper presents the award-winning RMIT-ADM+S system for the Text-to-Text track of the NeurIPS~2025 MMU-RAG Competition. We introduce Routing-to-RAG (R2RAG), a research-focused…
Stairway to Fairness: Connecting Group and Individual Fairness
Theresia Veronika Rampisela, Maria Maistro, Tuukka Ruotsalo +2
Fairness in recommender systems (RSs) is commonly categorised into group fairness and individual fairness. However, there is no established scientific understanding of the relation…
Demographically-Inspired Query Variants Using an LLM
Marwah Alaofi, Nicola Ferro, Paul Thomas +2
This study proposes a method to diversify queries in existing test collections to reflect some of the diversity of search engine users, aligning with an earlier vision of an 'ideal…
SenseSeek Dataset: Multimodal Sensing to Study Information Seeking Behaviors
Kaixin Ji, Danula Hettiachchi, Falk Scholer +2
Information processing tasks involve complex cognitive mechanisms that are shaped by various factors, including individual goals, prior experience, and system environments. Underst…
The Effects of Demographic Instructions on LLM Personas
Angel Felipe Magnossão de Paula, J. Shane Culpepper, Alistair Moffat +3
Social media platforms must filter sexist content in compliance with governmental regulations. Current machine learning approaches can reliably detect sexism based on standardized…
Control Search Rankings, Control the World: What is a Good Search Engine?
Simon Coghlan, Hui Xian Chia, Falk Scholer +1
This paper examines the ethical question, 'What is a good search engine?' Since search engines are gatekeepers of global online information, it is vital they do their job ethically…
Multi-stage Large Language Model Pipelines Can Outperform GPT-4o in Relevance Assessment
Julian A. Schnabel, Johanne R. Trippas, Falk Scholer +1
The effectiveness of search systems is evaluated using relevance labels that indicate the usefulness of documents for specific queries and users. While obtaining these relevance la…
Towards Investigating Biases in Spoken Conversational Search
Sachin Pathiyan Cherumanal, Falk Scholer, Johanne R. Trippas +1
Voice-based systems like Amazon Alexa, Google Assistant, and Apple Siri, along with the growing popularity of OpenAI's ChatGPT and Microsoft's Copilot, serve diverse populations, i…
Characterizing Information Seeking Processes with Multiple Physiological Signals
Kaixin Ji, Danula Hettiachchi, Flora D. Salim +2
Information access systems are getting complex, and our understanding of user behavior during information seeking processes is mainly drawn from qualitative methods, such as observ…
Towards Detecting and Mitigating Cognitive Bias in Spoken Conversational Search
Kaixin Ji, Sachin Pathiyan Cherumanal, Johanne R. Trippas +4
Instruments such as eye-tracking devices have contributed to understanding how users interact with screen-based search engines. However, user-system interactions in audio-only chan…
Online and Offline Evaluation in Search Clarification
Leila Tavakoli, Johanne R. Trippas, Hamed Zamani +2
The effectiveness of clarification question models in engaging users within search systems is currently constrained, casting doubt on their overall usefulness. To improve the perfo…
Walert: Putting Conversational Search Knowledge into Action by Building and Evaluating a Large Language Model-Powered Chatbot
Sachin Pathiyan Cherumanal, Lin Tian, Futoon M. Abushaqra +7
Creating and deploying customized applications is crucial for operational success and enriching user experiences in the rapidly evolving modern business world. A prominent facet of…