31 citations · 58 across the 30 of their papers we have counts for
11 papers · 1 filter
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…
Not All Transparency Is Equal: Source Presentation Effects on Attention, Interaction, and Persuasion in Conversational Search
Jiangen He, Jiqun Liu
Conversational search systems increasingly provide source citations, yet how citation or source presentation formats influence user engagement remains unclear. We conducted a crowd…
Your plan may succeed, but what about failure? Investigating how people use ChatGPT for long-term life task planning
Ben Wang, Jiqun Liu
Long-term life task planning is inherently complex and uncertain, yet little is known about how emerging AI systems support this process. This study investigates how people use Cha…
Mitigating the Threshold Priming Effect in Large Language Model-Based Relevance Judgments via Personality Infusing
Nuo Chen, Hanpei Fang, Jiqun Liu +3
Recent research has explored LLMs as scalable tools for relevance labeling, but studies indicate they are susceptible to priming effects, where prior relevance judgments influence…
The Bias is in the Details: An Assessment of Cognitive Bias in LLMs
R. Alexander Knipper, Charles S. Knipper, Kaiqi Zhang +3
As Large Language Models (LLMs) are increasingly embedded in real-world decision-making processes, it becomes crucial to examine the extent to which they exhibit cognitive biases.…
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…
Human-Centered Explainability in Interactive Information Systems: A Survey
Yuhao Zhang, Jiaxin An, Ben Wang +2
Human-centered explainability has become a critical foundation for the responsible development of interactive information systems, where users must be able to understand, interpret…
Trapped by Expectations: Functional Fixedness in LLM-Enabled Chat Search
Jiqun Liu, Jamshed Karimnazarov, Ryen W. White
Functional fixedness, a cognitive bias that restricts users' interactions with a new system or tool to expected or familiar ways, limits the full potential of Large Language Model…
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…
Investigating the Impact of LLM Personality on Cognitive Bias Manifestation in Automated Decision-Making Tasks
Jiangen He, Jiqun Liu
Large Language Models (LLMs) are increasingly used in decision-making, yet their susceptibility to cognitive biases remains a pressing challenge. This study explores how personalit…