31 citations · 58 across the 30 of their papers we have counts for
31 papers
Beyond Local Surprise: Grounded Dialogue as Selective Belief Revision under Referential Uncertainty
Ziming Liu, Bhanu Chaitanya Jasti, Ziyang Xu +3
When a speaker refers to a scene that the listener cannot directly see, the listener must decide whether to preserve its current understanding or revise it as new utterances arrive…
Search or Chat? Comparing How We Learn About Debated Topics
Ran Yu, Alisa Rieger, Rabia Karatoprak Ersen +1
As large language models (LLMs) become more integrated into everyday information platforms, chat-based systems are emerging as a popular alternative to traditional web searches, es…
Experiences Build Characters: The Linguistic Origins and Functional Impact of LLM Personality
Xi Wang, Mengdie Zhuang, Jiqun Liu
Human problem-solving is enriched by a diversity of styles and personality traits, yet the development of Large Language Models (LLMs) has largely prioritized uniform performance b…
Stability Regimes for Framing-Sensitive Fine-Tuning in Language Models
Victor De Lima, Jiqun Liu, Grace Hui Yang
AI systems increasingly shape how individuals access and evaluate information, intensifying concerns about misinformation. Computational simulation offers a controlled complement t…
ECHO: An Open Research Platform for Evaluation of Chat, Human Behavior, and Outcomes
Jiqun Liu, Nischal Dinesh, Ran Yu
ECHO (Evaluation of Chat, Human behavior, and Outcomes) is an open research platform designed to support reproducible, mixed-method studies of human interaction with both conversat…
Seeing to Think? How Source Transparency Design Shapes Interactive Information Seeking and Evaluation in Conversational AI
Jiangen He, Jiqun Liu
Conversational AI systems increasingly function as primary interfaces for information seeking, yet how they present sources to support information evaluation remains under-explored…
Bounded Minds, Generative Machines: Envisioning Conversational AI that Works with Human Heuristics and Reduces Bias Risk
Jiqun Liu
Conversational AI is rapidly becoming a primary interface for information seeking and decision making, yet most systems still assume idealized users. In practice, human reasoning i…
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…
Judging with Personality and Confidence: A Study on Personality-Conditioned LLM Relevance Assessment
Nuo Chen, Hanpei Fang, Piaohong Wang +3
Recent studies have shown that prompting can enable large language models (LLMs) to simulate specific personality traits and produce behaviors that align with those traits. However…
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…
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…
AI Can Be Cognitively Biased: An Exploratory Study on Threshold Priming in LLM-Based Batch Relevance Assessment
Nuo Chen, Jiqun Liu, Xiaoyu Dong +3
Cognitive biases are systematic deviations in thinking that lead to irrational judgments and problematic decision-making, extensively studied across various fields. Recently, large…