31 citations · 58 across the 30 of their papers we have counts for
12 papers · 1 filter
Re-Rankers as Relevance Judges
Chuan Meng, Jiqun Liu, Mohammad Aliannejadi +3
Using large language models (LLMs) to predict relevance judgments has shown promising results. Most studies treat this task as a distinct research line, e.g., focusing on prompt de…
Improving Data Reusability in Interactive Information Retrieval: Insights from the Community
Tianji Jiang, Wenqi Li, Jiqun Liu
In this study, we conducted semi-structured interviews with 21 IIR researchers to investigate their data reuse practices. This study aims to expand upon current findings by explori…
TRUE: A Reproducible Framework for LLM-Driven Relevance Judgment in Information Retrieval
Mouly Dewan, Jiqun Liu, Chirag Shah
LLM-based relevance judgment generation has become a crucial approach in advancing evaluation methodologies in Information Retrieval (IR). It has progressed significantly, often sh…
LLM-Driven Usefulness Judgment for Web Search Evaluation
Mouly Dewan, Jiqun Liu, Aditya Gautam +1
Evaluation is fundamental in optimizing search experiences and supporting diverse user intents in Information Retrieval (IR). Traditional search evaluation methods primarily rely o…
LLM-Driven Usefulness Labeling for IR Evaluation
Mouly Dewan, Jiqun Liu, Chirag Shah
In the information retrieval (IR) domain, evaluation plays a crucial role in optimizing search experiences and supporting diverse user intents. In the recent LLM era, research has…
Who Gets Recommended? Investigating Gender, Race, and Country Disparities in Paper Recommendations from Large Language Models
Yifan Tian, Yixin Liu, Yi Bu +1
This paper investigates the performance of several representative large models in the tasks of literature recommendation and explores potential biases in research exposure. The res…
The Landscape of Data Reuse in Interactive Information Retrieval: Motivations, Sources, and Evaluation of Reusability
Tianji Jiang, Wenqi Li, Jiqun Liu
Sharing and reusing research data can effectively reduce redundant efforts in data collection and curation, especially for small labs and research teams conducting human-centered s…
The Decoy Dilemma in Online Medical Information Evaluation: A Comparative Study of Credibility Assessments by LLM and Human Judges
Jiqun Liu, Jiangen He
Can AI be cognitively biased in automated information judgment tasks? Despite recent progresses in measuring and mitigating social and algorithmic biases in AI and large language m…
Report on the Workshop on Simulations for Information Access (Sim4IA 2024) at SIGIR 2024
Timo Breuer, Christin Katharina Kreutz, Norbert Fuhr +15
This paper is a report of the Workshop on Simulations for Information Access (Sim4IA) workshop at SIGIR 2024. The workshop had two keynotes, a panel discussion, nine lightning talk…
Decoy Effect In Search Interaction: Understanding User Behavior and Measuring System Vulnerability
Nuo Chen, Jiqun Liu, Hanpei Fang +3
This study examines the decoy effect's underexplored influence on user search interactions and methods for measuring information retrieval (IR) systems' vulnerability to this effec…
Cognitively Biased Users Interacting with Algorithmically Biased Results in Whole-Session Search on Debated Topics
Ben Wang, Jiqun Liu
When interacting with information retrieval (IR) systems, users, affected by confirmation biases, tend to select search results that confirm their existing beliefs on socially sign…
Decoy Effect in Search Interaction: A Pilot Study
Nuo Chen, Jiqun Liu, Tetsuya Sakai +1
In recent years, the influence of cognitive effects and biases on users' thinking, behaving, and decision-making has garnered increasing attention in the field of interactive infor…