77 citations · 158 across the 21 of their papers we have counts for
11 papers · 1 filter
The "Curse of Knowledge" in LLM Query Simulation: Concept Provenance for Tracing Answer-Side Intrusion
Chenglong Ma, Xinye Wanyan, Danula Hettiachchi +2
LLM-generated search queries are widely used to augment IR evaluation, yet they may contain concepts that presuppose answer-side document knowledge, violating the information-acces…
Verifiable User Simulation for Search and Recommendation Systems
Chenglong Ma, Xinye Wanyan, Danula Hettiachchi +3
Large-language-model (LLM) based user simulation is increasingly adopted for evaluating search engines, recommender systems, and retrieval-augmented generation pipelines, yet most…
Task-Aware Automated User Profile Generation for Recommendation Simulation Using Large Language Models
Xinye Wanyan, Chenglong Ma, Danula Hettiachchi +2
Large Language Model (LLM)-based agent simulation has emerged as a promising approach to meet the increasing demand for real-time and rigorous evaluation in modern recommender syst…
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…
ISMIE: A Framework to Characterize Information Seeking in Modern Information Environments
Shuoqi Sun, Danula Hettiachchi, Damiano Spina
The modern information environment (MIE) is increasingly complex, shaped by a wide range of techniques designed to satisfy users' information needs. Information seeking (IS) models…
Temporal-Aware User Behaviour Simulation with Large Language Models for Recommender Systems
Xinye Wanyan, Danula Hettiachchi, Chenglong Ma +2
Large Language Models (LLMs) demonstrate human-like capabilities in language understanding, reasoning, and generation, driving interest in using LLM-based agents to simulate human…
PUB: An LLM-Enhanced Personality-Driven User Behaviour Simulator for Recommender System Evaluation
Chenglong Ma, Ziqi Xu, Yongli Ren +2
Traditional offline evaluation methods for recommender systems struggle to capture the complexity of modern platforms due to sparse behavioural signals, noisy data, and limited mod…
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…
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…
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…
Designing and Evaluating Presentation Strategies for Fact-Checked Content
Danula Hettiachchi, Kaixin Ji, Jenny Kennedy +5
With the rapid growth of online misinformation, it is crucial to have reliable fact-checking methods. Recent research on finding check-worthy claims and automated fact-checking hav…