arXiv subjects

Jiqun Liu

Publications and source records attributed to Jiqun Liu.

At least 19 recordsLinked to original sources

arXiv2026

Beyond Local Surprise: Grounded Dialogue as Selective Belief Revision under Referential Uncertainty

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. Many language systems treat local mismatch as a cue for updating: divergence from the current understanding encourages adjustment. Yet conversational understanding may be more conservative, interpreting mismatching evidence relative to prior understanding rather than immediately revising it. We introduce a controlled, data-driven framework for turn-by-turn preserve/revise decisions in dialogue, where competing revision policies are learned under otherwise identical conditions. We compare four theory-driven revision strategies, each reflecting a different assumption about when listeners should preserve or revise. Two findings stand out. First, a mismatch-driven policy that updates solely based on local divergence reacts strongly to mismatch but destabilizes grounding and degrades retrieval. Second, an uncertainty-sensitive policy extends mismatch-based updating with accumulated evidence, preserving coherent understanding while maintaining strong retrieval performance. Surprisingly, coherent understanding emerges from a counterintuitive pattern: local mismatch promotes preservation, whereas accumulated uncertainty promotes revision, suggesting that listeners maintain prior understanding despite local mismatch and revise only when uncertainty sufficiently accumulates. This pattern is consistent with conceptual pact theory.

cs.CL↗

arXiv2026

Search or Chat? Comparing How We Learn About Debated Topics

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, especially for informational search and informal learning tasks. Despite this shift, little is known about how different tools affect learning outcomes. Our work aims to improve the understanding of how chat-based information access supports and impacts learning performance in informal learning settings. In this paper, we present the results of a crowdsourcing user study (N = 194) that compares learning about debated topics using a traditional search interface versus an LLM-powered chat interface. Through our analysis of learning outcomes, user characteristics, and interaction patterns, we found no significant differences in user learning gain or critical reflection on our study tasks. Our observations from the analysis of further exploratory variables suggest that, in the context of longstanding debated topics, user characteristics such as their attitude strength and level of intellectual humility might be more important in shaping immediate learning outcomes than the information access tool.

cs.HC↗

arXiv2026

Experiences Build Characters: The Linguistic Origins and Functional Impact of LLM Personality

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 benchmarks that favour specific behavioural tendencies such as assertiveness. To investigate how diverse experiences shape machine personality and influence problem-solving, this study employs continued pre-training to expose models to domain-specific texts in an unsupervised manner, simulating the accumulation of experience. By adapting the Big Five framework via the Machine Personality Inventory (MPI), we quantify the personality traits of these model variants and analyse their relationship to linguistic style and reasoning behaviour. The findings reveal that model competence is bimodal, peaking at "Expressive Generalists" and "Suppressed Specialists," while identifying a "Suppression Advantage" where reduced social traits enhance complex reasoning performance. This study further establishes a causal link between training data linguistics, such as imperative frequency, and lexical diversity, providing a roadmap for "Personality Engineering".

cs.CL↗

arXiv2026

FrameRef: A Framing Dataset and Simulation Testbed for Modeling Bounded Rational Information Health

Information ecosystems increasingly shape how people internalize exposure to adverse digital experiences, raising concerns about the long-term consequences for information health. In modern search and recommendation systems, ranking and personalization policies play a central role in shaping such exposure and its long-term effects on users. To study these effects in a controlled setting, we present FrameRef, a large-scale dataset of 1,073,740 systematically reframed claims across five framing dimensions: authoritative, consensus, emotional, prestige, and sensationalist, and propose a simulation-based framework for modeling sequential information exposure and reinforcement dynamics characteristic of ranking and recommendation systems. Within this framework, we construct framing-sensitive agent personas by fine-tuning language models with framing-conditioned loss attenuation, inducing targeted biases while preserving overall task competence. Using Monte Carlo trajectory sampling, we show that small, systematic shifts in acceptance and confidence can compound over time, producing substantial divergence in cumulative information health trajectories. Human evaluation further confirms that FrameRef's generated framings measurably affect human judgment. Together, our dataset and framework provide a foundation for systematic information health research through simulation, complementing and informing responsible human-centered research. We release FrameRef, code, documentation, human evaluation data, and persona adapter models at https://github.com/infosenselab/frameref.

cs.CY↗

arXiv2026

ECHO: An Open Research Platform for Evaluation of Chat, Human Behavior, and Outcomes

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 conversational AI systems and Web search engines. It enables researchers from varying disciplines to orchestrate end-to-end experimental workflows that integrate consent and background surveys, chat-based and search-based information-seeking sessions, writing or judgment tasks, and pre- and post-task evaluations within a unified, low-coding-load framework. ECHO logs fine-grained interaction traces and participant responses, and exports structured datasets for downstream analysis. By supporting both chat and search alongside flexible evaluation instruments, ECHO lowers technical barriers for studying learning, decision making, and user experience across different information access paradigms, empowering researchers from information retrieval, HCI, and the social sciences to conduct scalable and reproducible human-centered AI evaluations.

cs.HC↗

arXiv2026

Seeing to Think? How Source Transparency Design Shapes Interactive Information Seeking and Evaluation in Conversational AI

Conversational AI systems increasingly function as primary interfaces for information seeking, yet how they present sources to support information evaluation remains under-explored. This paper investigates how source transparency design shapes interactive information seeking, trust, and critical engagement. We conducted a controlled between-subjects experiment (N=372) comparing four source presentation interfaces - Collapsible, Hover Card, Footer, and Aligned Sidebar - varying in visibility and accessibility. Using fine-grained behavioral analysis and automated critical thinking assessment, we found that interface design fundamentally alters exploration strategies and evidence integration. While the Hover Card interface facilitated seamless, on-demand verification during the task, the Aligned Sidebar uniquely mitigated the negative effects of information overload: as citation density increased, Sidebar users demonstrated significantly higher critical thinking and synthesis scores compared to other conditions. Our results highlight a trade-off between designs that support workflow fluency and those that enforce reflective verification, offering practical implications for designing adaptive and responsible conversational AI that fosters critical engagement with AI generated content.

cs.HC↗

arXiv2026

Bounded Minds, Generative Machines: Envisioning Conversational AI that Works with Human Heuristics and Reduces Bias Risk

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 is bounded by limited attention, uneven knowledge, and reliance on heuristics that are adaptive but bias-prone. This article outlines a research pathway grounded in bounded rationality, and argues that conversational AI should be designed to work with human heuristics rather than against them. It identifies key directions for detecting cognitive vulnerability, supporting judgment under uncertainty, and evaluating conversational systems beyond factual accuracy, toward decision quality and cognitive robustness.

cs.ET↗

arXiv2026

TRUE: A Reproducible Framework for LLM-Driven Relevance Judgment in Information Retrieval

LLM-based relevance judgment generation has become a crucial approach in advancing evaluation methodologies in Information Retrieval (IR). It has progressed significantly, often showing high correlation with human judgments as reflected in LLMJudge leaderboards \cite{rahmani2025judging}. However, existing methods for relevance judgments, rely heavily on sensitive prompting strategies, lacking standardized workflows for generating reliable labels. To fill this gap, we reintroduce our method, \textit{Task-aware Rubric-based Evaluation} (TRUE), for relevance judgment generation. Originally developed for usefulness evaluation in search sessions, we extend TRUE to mitigate the gap in relevance judgment due to its demonstrated effectiveness and reproducible workflow. This framework leverages iterative data sampling and reasoning to evaluate relevance judgments across multiple factors including intent, coverage, specificity, accuracy and usefulness. In this paper, we evaluate TRUE on the TREC DL 2019, 2020 and LLMJudge datasets and our results show that TRUE achieves strong performance on the system-ranking LLM leaderboards. The primary focus of this work is to provide a reproducible framework for LLM-based relevance judgments, and we further analyze the effectiveness of TRUE across multiple dimensions.

cs.IR↗

arXiv2026

Re-Rankers as Relevance Judges

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 design for predicting relevance labels given a query and passage. However, predicting relevance judgments is essentially a form of relevance prediction, a problem extensively studied in tasks such as re-ranking. Despite this potential overlap, little research has explored reusing or adapting established re-ranking methods to predict relevance judgments, leading to potential resource waste and redundant development. To bridge this gap, we reproduce re-rankers in a re-ranker-as-relevance-judge setup. We design two adaptation strategies: (i) using binary tokens (e.g., "true" and "false") generated by a re-ranker as direct judgments, and (ii) converting continuous re-ranking scores into binary labels via thresholding. We perform extensive experiments on TREC-DL 2019 to 2023 with 8 re-rankers from 3 families, ranging from 220M to 32B, and analyse the evaluation bias exhibited by re-ranker-based judges. Results show that re-ranker-based relevance judges, under both strategies, can outperform UMBRELA, a state-of-the-art LLM-based relevance judge, in around 40% to 50% of the cases; they also exhibit strong self-preference towards their own and same-family re-rankers, as well as cross-family bias.

cs.IR↗

arXiv2026

Judging with Personality and Confidence: A Study on Personality-Conditioned LLM Relevance Assessment

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, there is limited understanding of how these simulated personalities influence critical web search decisions, specifically relevance assessment. Moreover, few studies have examined how simulated personalities impact confidence calibration, specifically the tendencies toward overconfidence or underconfidence. This gap exists even though psychological literature suggests these biases are trait-specific, often linking high extraversion to overconfidence and high neuroticism to underconfidence. To address this gap, we conducted a comprehensive study evaluating multiple LLMs, including commercial models and open-source models, prompted to simulate Big Five personality traits. We tested these models across three test collections (TREC DL 2019, TREC DL 2020, and LLMJudge), collecting two key outputs for each query-document pair: a relevance judgment and a self-reported confidence score. The findings show that personalities such as low agreeableness consistently align more closely with human labels than the unprompted condition. Additionally, low conscientiousness performs well in balancing the suppression of both overconfidence and underconfidence. We also observe that relevance scores and confidence distributions vary systematically across different personalities. Based on the above findings, we incorporate personality-conditioned scores and confidence as features in a random forest classifier. This approach achieves performance that surpasses the best single-personality condition on a new dataset (TREC DL 2021), even with limited training data. These findings highlight that personality-derived confidence offers a complementary predictive signal, paving the way for more reliable and human-aligned LLM evaluators.

cs.CL↗

arXiv2025

Improving Data Reusability in Interactive Information Retrieval: Insights from the Community

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 exploring IIR researchers' information-obtaining behaviors regarding data reuse. We identified the information about shared data characteristics that IIR researchers need when evaluating data reusability, as well as the sources they typically consult to obtain this information. We consider this work to be an initial step toward revealing IIR researchers' data reuse practices and identifying what the community needs to do to promote data reuse. We hope that this study, as well as future research, will inspire more individuals to contribute to ongoing efforts aimed at designing standards, infrastructures, and policies, as well as fostering a sustainable culture of data sharing and reuse in this field.

cs.IR↗

arXiv2025

Not All Transparency Is Equal: Source Presentation Effects on Attention, Interaction, and Persuasion in Conversational Search

Conversational search systems increasingly provide source citations, yet how citation or source presentation formats influence user engagement remains unclear. We conducted a crowdsourcing user experiment with 394 participants comparing four source presentation designs that varied citation visibility and accessibility: collapsible lists, hover cards, footer lists, and aligned sidebars.High-visibility interfaces generated substantially more hovering on sources, though clicking remained infrequent across all conditions. While interface design showed limited effects on user experience and perception measures, it significantly influenced knowledge, interest, and agreement changes. High-visibility interfaces initially reduced knowledge gain and interest, but these positive effects emerged with increasing source usage. The sidebar condition uniquely increased agreement change. Our findings demonstrate that source presentation alone may not enhance engagement and can even reduce it when insufficient sources are provided.

cs.HC↗

arXiv2025

Your plan may succeed, but what about failure? Investigating how people use ChatGPT for long-term life task planning

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 ChatGPT for such planning tasks, focusing on user practices, uncertainties, and perceptions of AI assistance. We conducted an interview study with 14 participants who engaged in long-term planning activities using ChatGPT, combining analysis of their prompts and interview responses. The task topics across diverse domains, including personal well-being, event planning, and professional learning, along with prompts to initiate, refine, and contextualize plans. ChatGPT helped structure complex goals into manageable steps, generate ideas, and sustain motivation, serving as a reflective partner. Yet its outputs were often generic or idealized, lacking personalization, contextual realism, and adaptability, requiring users to actively adapt and verify results. Participants expressed a need for AI systems that provide adaptive and trustworthy guidance while acknowledging uncertainty and potential failure in long-term planning. Our findings show how AI supports long-term life task planning under evolving uncertainty and highlight design implications for systems that are adaptive, uncertainty-aware, and capable of supporting long-term planning as an evolving human-AI collaboration.

cs.HC↗

arXiv2025

Mitigating the Threshold Priming Effect in Large Language Model-Based Relevance Judgments via Personality Infusing

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 later ones. Although psychological theories link personality traits to such biases, it is unclear whether simulated personalities in LLMs exhibit similar effects. We investigate how Big Five personality profiles in LLMs influence priming in relevance labeling, using multiple LLMs on TREC 2021 and 2022 Deep Learning Track datasets. Our results show that certain profiles, such as High Openness and Low Neuroticism, consistently reduce priming susceptibility. Additionally, the most effective personality in mitigating priming may vary across models and task types. Based on these findings, we propose personality prompting as a method to mitigate threshold priming, connecting psychological evidence with LLM-based evaluation practices.

cs.CL↗

arXiv2025

LLM-Driven Usefulness Labeling for IR Evaluation

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 been conducted to automate document relevance labels, as these labels have traditionally been assigned by crowd-sourced workers - a process that is both time and consuming and costly. This study focuses on LLM-generated usefulness labels, a crucial evaluation metric that considers the user's search intents and task objectives, an aspect where relevance falls short. Our experiment utilizes task-level, query-level, and document-level features along with user search behavior signals, which are essential in defining the usefulness of a document. Our research finds that (i) pre-trained LLMs can generate moderate usefulness labels by understanding the comprehensive search task session, (ii) pre-trained LLMs perform better judgement in short search sessions when provided with search session contexts. Additionally, we investigated whether LLMs can capture the unique divergence between relevance and usefulness, along with conducting an ablation study to identify the most critical metrics for accurate usefulness label generation. In conclusion, this work explores LLM-generated usefulness labels by evaluating critical metrics and optimizing for practicality in real-world settings.

cs.IR↗

arXiv2025

Human-Centered Explainability in Interactive Information Systems: A Survey

Human-centered explainability has become a critical foundation for the responsible development of interactive information systems, where users must be able to understand, interpret, and scrutinize AI-driven outputs to make informed decisions. This systematic survey of literature aims to characterize recent progress in user studies on explainability in interactive information systems by reviewing how explainability has been conceptualized, designed, and evaluated in practice. Following PRISMA guidelines, eight academic databases were searched, and 100 relevant articles were identified. A structural encoding approach was then utilized to extract and synthesize insights from these articles. The main contributions include 1) five dimensions that researchers have used to conceptualize explainability; 2) a classification scheme of explanation designs; 3) a categorization of explainability measurements into six user-centered dimensions. The review concludes by reflecting on ongoing challenges and providing recommendations for future exploration of related issues. The findings shed light on the theoretical foundations of human-centered explainability, informing the design of interactive information systems that better align with diverse user needs and promoting the development of systems that are transparent, trustworthy, and accountable.

cs.HC↗

arXiv2025

LLM-Driven Usefulness Judgment for Web Search Evaluation

Evaluation is fundamental in optimizing search experiences and supporting diverse user intents in Information Retrieval (IR). Traditional search evaluation methods primarily rely on relevance labels, which assess how well retrieved documents match a user's query. However, relevance alone fails to capture a search system's effectiveness in helping users achieve their search goals, making usefulness a critical evaluation criterion. In this paper, we explore an alternative approach: LLM-generated usefulness labels, which incorporate both implicit and explicit user behavior signals to evaluate document usefulness. We propose Task-aware Rubric-based Usefulness Evaluation (TRUE), a rubric-driven evaluation method that employs iterative sampling and reasoning to model complex search behavior patterns. Our findings show that (i) LLMs can generate moderate usefulness labels by leveraging comprehensive search session history incorporating personalization and contextual understanding, and (ii) fine-tuned LLMs improve usefulness judgments when provided with structured search session contexts. Additionally, we examine whether LLMs can distinguish between relevance and usefulness, particularly in cases where this divergence impacts search success. We also conduct an ablation study to identify key metrics for accurate usefulness label generation, optimizing for token efficiency and cost-effectiveness in real-world applications. This study advances LLM-based usefulness evaluation by refining key user metrics, exploring LLM-generated label reliability, and ensuring feasibility for large-scale search systems.

cs.IR↗

arXiv2025

Trapped by Expectations: Functional Fixedness in LLM-Enabled Chat Search

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)-enabled chat search, especially in complex and exploratory tasks. To investigate its impact, we conducted a crowdsourcing study with 450 participants, each completing one of six decision-making tasks spanning public safety, diet and health management, sustainability, and AI ethics. Participants engaged in a multi-prompt conversation with ChatGPT to address the task, allowing us to compare pre-chat intent-based expectations with observed interactions. We found that: 1) Several aspects of pre-chat expectations are closely associated with users' prior experiences with ChatGPT, search engines, and virtual assistants; 2) Prior system experience shapes language use and prompting behavior. Frequent ChatGPT users reduced deictic terms and hedge words and frequently adjusted prompts. Users with rich search experience maintained structured, less-conversational queries with minimal modifications. Users of virtual assistants favored directive, command-like prompts, reinforcing functional fixedness; 3) When the system failed to meet expectations, participants generated more detailed prompts with increased linguistic diversity, reflecting adaptive shifts. These findings suggest that while preconceived expectations constrain early interactions, unmet expectations can motivate behavioral adaptation. With appropriate system support, this may promote broader exploration of LLM capabilities. This work also introduces a typology for user intents in chat search and highlights the importance of mitigating functional fixedness to support more creative and analytical use of LLMs.

cs.HC↗