2 citations · 2 across the 11 of their papers we have counts for
12 papers
MetaPersona: Task-Grounded Synthetic Populations from Empirical Social Science
Jinyi Ye, Yuangang Li, Chenxiao Yu +7
Personas used to seed LLM social simulations face a cold-start problem: existing methods lack a principled basis for deciding which attributes to include and how to assign their va…
De-conflating Preference and Qualification: Constrained Dual-Perspective Reasoning for Job Recommendation with Large Language Models
Bryce Kan, Wei Yang, Emily Nguyen +4
Professional job recommendation involves a complex bipartite matching process that must reconcile a candidate's subjective preference with an employer's objective qualification. Wh…
"Someone Hid It": Query-Agnostic Black-Box Attacks on LLM-Based Retrieval
Jiate Li, Defu Cao, Li Li +8
Large language models (LLMs) have been serving as effective backbones for retrieval systems, including Retrieval-Augmentation-Generation (RAG), Dense Information Retriever (IR), an…
Defenses Against Prompt Attacks Learn Surface Heuristics
Shawn Li, Chenxiao Yu, Zhiyu Ni +4
Large language models (LLMs) are increasingly deployed in security-sensitive applications, where they must follow system- or developer-specified instructions that define the intend…
Multimodal Generative Engine Optimization: Rank Manipulation for Vision-Language Model Rankers
Yixuan Du, Chenxiao Yu, Haoyan Xu +3
Vision-Language Models (VLMs) integrate visual and textual knowledge into unified representations that increasingly underpin modern retrieval and recommendation systems. However, i…
Tracing Moral Foundations in Large Language Models
Chenxiao Yu, Bowen Yi, Farzan Karimi-Malekabadi +5
Large language models often produce human-like moral judgments, but it is unclear whether this reflects an internal conceptual structure or superficial ``moral mimicry.'' Using Mor…
Value-Action Alignment in Large Language Models under Privacy-Prosocial Conflict
Guanyu Chen, Chenxiao Yu, Xiyang Hu
Large language models (LLMs) are increasingly used to simulate decision-making tasks involving personal data sharing, where privacy concerns and prosocial motivations can push choi…
Mitigating Hallucinations in Large Language Models via Causal Reasoning
Yuangang Li, Yiqing Shen, Yi Nian +7
Large language models (LLMs) exhibit logically inconsistent hallucinations that appear coherent yet violate reasoning principles, with recent research suggesting an inverse relatio…
StealthRank: LLM Ranking Manipulation via Stealthy Prompt Optimization
Yiming Tang, Yi Fan, Chenxiao Yu +3
The integration of large language models (LLMs) into information retrieval systems introduces new attack surfaces, particularly for adversarial ranking manipulations. We present $\…
Political-LLM: Large Language Models in Political Science
Lincan Li, Jiaqi Li, Catherine Chen +44
In recent years, large language models (LLMs) have been widely adopted in political science tasks such as election prediction, sentiment analysis, policy impact assessment, and mis…
A Large-Scale Simulation on Large Language Models for Decision-Making in Political Science
Chenxiao Yu, Jinyi Ye, Yuangang Li +4
While LLMs have demonstrated remarkable capabilities in text generation and reasoning, their ability to simulate human decision-making -- particularly in political contexts -- rema…
Towards More Accurate US Presidential Election via Multi-step Reasoning with Large Language Models
Chenxiao Yu, Zhaotian Weng, Yuangang Li +3
Can Large Language Models (LLMs) accurately predict election outcomes? While LLMs have demonstrated impressive performance in various domains, including healthcare, legal analysis,…