9 citations · 17 across the 13 of their papers we have counts for
14 papers
Ethics and Social Responsibility in AI-Assisted Interviewing: An LLM-in-the-Loop Study of AI-Generated Follow-Up Questions
He Zhang, Yueyan Liu, Xin Guan +2
Semi-structured interviews rely on timely, context-sensitive follow-up questions, yet interviewers' cognitive load and limited domain familiarity can constrain probing depth. We re…
NormCode Canvas: Making LLM Agentic Workflows Development Sustainable via Case-Based Reasoning
Xin Guan, Yunshan Li, Ze Wang
We present NormCode Canvas (v1.1.3), a deployed system realizing Case-Based Reasoning at two levels for multi-step LLM workflows. The foundation is NormCode, a semi-formal planning…
Evaluating 21st-Century Competencies in Postsecondary Curricula with Large Language Models: Performance Benchmarking and Reasoning-Based Prompting Strategies
Zhen Xu, Xin Guan, Chenxi Shi +2
The growing emphasis on 21st-century competencies in postsecondary education, intensified by the transformative impact of generative AI, underscores the need to evaluate how these…
NormCode: A Semi-Formal Language for Auditable AI Planning
Xin Guan, Yunshan Li, Zekun Wu +1
As AI systems move into high stakes domains such as legal reasoning, medical diagnosis, and financial decision making, regulators and practitioners increasingly demand auditability…
Harnessing the Power of AI in Qualitative Research: Role Assignment, Engagement, and User Perceptions of AI-Generated Follow-Up Questions in Semi-Structured Interviews
He Zhang, Yueyan Liu, Xin Guan +2
Semi-structured interviews highly rely on the quality of follow-up questions, yet interviewers' knowledge and skills may limit their depth and potentially affect outcomes. While ma…
MPF: Aligning and Debiasing Language Models post Deployment via Multi Perspective Fusion
Xin Guan, PeiHsin Lin, Zekun Wu +4
Multiperspective Fusion (MPF) is a novel posttraining alignment framework for large language models (LLMs) developed in response to the growing need for easy bias mitigation. Built…
LibVulnWatch: A Deep Assessment Agent System and Leaderboard for Uncovering Hidden Vulnerabilities in Open-Source AI Libraries
Zekun Wu, Seonglae Cho, Umar Mohammed +7
Open-source AI libraries are foundational to modern AI systems, yet they present significant, underexamined risks spanning security, licensing, maintenance, supply chain integrity,…
Web-Bench: A LLM Code Benchmark Based on Web Standards and Frameworks
Kai Xu, YiWei Mao, XinYi Guan +1
The application of large language models (LLMs) in the field of coding is evolving rapidly: from code assistants, to autonomous coding agents, and then to generating complete proje…
A Computational Cognitive Model for Processing Repetitions of Hierarchical Relations
Zeng Ren, Xinyi Guan, Martin Rohrmeier
Patterns are fundamental to human cognition, enabling the recognition of structure and regularity across diverse domains. In this work, we focus on structural repeats, patterns tha…
Assessing Bias in Metric Models for LLM Open-Ended Generation Bias Benchmarks
Nathaniel Demchak, Xin Guan, Zekun Wu +3
Open-generation bias benchmarks evaluate social biases in Large Language Models (LLMs) by analyzing their outputs. However, the classifiers used in analysis often have inherent bia…
Bias Amplification: Large Language Models as Increasingly Biased Media
Ze Wang, Zekun Wu, Jeremy Zhang +5
Model collapse, a phenomenon characterized by performance degradation due to iterative training on synthetic data, has been widely studied. However, its implications for bias ampli…
SAGED: A Holistic Bias-Benchmarking Pipeline for Language Models with Customisable Fairness Calibration
Xin Guan, Ze Wang, Nathaniel Demchak +5
The development of unbiased large language models is widely recognized as crucial, yet existing benchmarks fall short in detecting biases due to limited scope, contamination, and l…
HyPA-RAG: A Hybrid Parameter Adaptive Retrieval-Augmented Generation System for AI Legal and Policy Applications
Rishi Kalra, Zekun Wu, Ayesha Gulley +4
Large Language Models (LLMs) face limitations in AI legal and policy applications due to outdated knowledge, hallucinations, and poor reasoning in complex contexts. Retrieval-Augme…
JobFair: A Framework for Benchmarking Gender Hiring Bias in Large Language Models
Ze Wang, Zekun Wu, Xin Guan +6
The use of Large Language Models (LLMs) in hiring has led to legislative actions to protect vulnerable demographic groups. This paper presents a novel framework for benchmarking hi…