26 citations · 38 across the 16 of their papers we have counts for
16 papers
The Evaluator Is Part of the Experiment: Measuring Open-Ended LLM Conformity
Alicia Guerra, Yibo Hu
Prior work on LLM conformity largely measures discrete answer flips under verifiable labels. Open-ended revisions require a different measurement strategy because answer quality is…
Social Pressure Breaks Majority Voting in LLM Safety Panels
Yibo Hu, Jiaming Qu
Large language models (LLMs) are increasingly used to detect unsafe content. A common approach is to combine judgments from a panel of models to correct individual mistakes, but th…
Early Detection of Distributed Backdoors in Multi-Agent LLM Systems: A Characterization Study
Diego Fernandez Arias, Dev Prashant Mistry, Ren Wang +1
Multi-agent LLM systems can be attacked by a payload that no single agent ever holds in full: several poisoned tools each hide one encrypted fragment, spreading them across several…
When Local Monitors Miss Compositional Harm: Diagnosing Distributed Backdoors in Multi-Agent Systems
Yibo Hu, Ren Wang
As multi-agent, tool-using LLM systems are deployed, a common safety net is a runtime monitor that checks each message, tool call, or step on its own. We show this net has a fundam…
Most LLM Conformity Needs No Speaker: Measuring the Speaker-Free Floor in Peer-Pressure Benchmarks
Yibo Hu, Jiaming Qu
LLM conformity is often used to describe cases where a model changes a correct answer toward a peer or group response. We show that most of this apparent conformity survives even a…
Easier to Mislead Than to Correct: Harmful and Beneficial Revision in LLM Conformity
Jiaming Qu, Lucheng Fu, Yibo Hu
Large language models are increasingly used in multi-agent systems, where they see and respond to other agents' answers. A key risk is conformity: a model may abandon its own answe…
When Surface Form Changes Moderation Decisions: A Paired Study of Code-Mixed Workflow Instability
Suraj Babu Thimma Krishnaram, Yibo Hu, Karthikeyan Saravanan
Hate moderation is often evaluated as classification on clean English inputs, but deployed systems must route content to actions such as ALLOW, FLAG, or REVIEW. We study how this w…
Can I Take Another Dose? Evaluating LLM Decision-Making Under Temporal Uncertainty in OTC Dosing QA
Maroof Kousar, Yibo Hu
Large language models (LLMs) are increasingly used for everyday health questions, including whether a user can safely take another dose of an over-the-counter (OTC) medication. Yet…
When Better Codebooks Are Not Enough: Predictive Performance and Behavioral Reliability in LLM Political Event Coding
Zixian He, Bharath Raahul Murugesan, Patrick Brandt +1
High accuracy does not necessarily make an LLM a faithful coder. This issue matters because many social-science studies rely on expert-written codebooks to turn text into structure…
The Influence of Text Variation on User Engagement in Cross-Platform Content Sharing
Yibo Hu, Yiqiao Jin, Meng Ye +2
In today's cross-platform social media landscape, understanding factors that drive engagement for multimodal content, especially text paired with visuals, remains complex. This stu…
Better to Ask in English: Cross-Lingual Evaluation of Large Language Models for Healthcare Queries
Yiqiao Jin, Mohit Chandra, Gaurav Verma +3
Large language models (LLMs) are transforming the ways the general public accesses and consumes information. Their influence is particularly pronounced in pivotal sectors like heal…
A Survey on the Role of Crowds in Combating Online Misinformation: Annotators, Evaluators, and Creators
Bing He, Yibo Hu, Yeon-Chang Lee +3
Online misinformation poses a global risk with significant real-world consequences. To combat misinformation, current research relies on professionals like journalists and fact-che…
Leveraging Codebook Knowledge with NLI and ChatGPT for Zero-Shot Political Relation Classification
Yibo Hu, Erick Skorupa Parolin, Latifur Khan +3
Is it possible accurately classify political relations within evolving event ontologies without extensive annotations? This study investigates zero-shot learning methods that use e…
Controllable Fake Document Infilling for Cyber Deception
Yibo Hu, Yu Lin, Erick Skorupa Parolin +2
Recent works in cyber deception study how to deter malicious intrusion by generating multiple fake versions of a critical document to impose costs on adversaries who need to identi…
Uncertainty-Aware Reliable Text Classification
Yibo Hu, Latifur Khan
Deep neural networks have significantly contributed to the success in predictive accuracy for classification tasks. However, they tend to make over-confident predictions in real-wo…
Multidimensional Uncertainty-Aware Evidential Neural Networks
Yibo Hu, Yuzhe Ou, Xujiang Zhao +2
Traditional deep neural networks (NNs) have significantly contributed to the state-of-the-art performance in the task of classification under various application domains. However,…