activity
20242026
most citedWho Gets Cited? Gender- and Majority-Bias in LLM-Driven Reference Selection

2 citations · 3 across the 4 of their papers we have counts for

collaborators

5 papers

cs.HC2026

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…

cs.HC20251 cited

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…

cs.DL20252 cited

Who Gets Cited? Gender- and Majority-Bias in LLM-Driven Reference Selection

Jiangen He

Large language models (LLMs) are rapidly being adopted as research assistants, particularly for literature review and reference recommendation, yet little is known about whether th…

cs.AI2025

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…

cs.IR2024

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…