1 citations · 1 across the 4 of their papers we have counts for
8 papers
Small Changes, Big Impact: Demographic Bias in LLM-Based Hiring Through Subtle Sociocultural Markers in Anonymised Resumes
Bryan Chen Zhengyu Tan, Shaun Khoo, Bich Ngoc Doan +3
Large Language Models (LLMs) are increasingly deployed in resume screening pipelines. Although explicit PII (e.g., names) is commonly redacted, resumes typically retain subtle soci…
Can Persona-Prompted LLMs Emulate Subgroup Values? An Empirical Analysis of Generalisability and Fairness in Cultural Alignment
Bryan Chen Zhengyu Tan, Zhengyuan Liu, Xiaoyuan Yi +4
Despite their global prevalence, many Large Language Models (LLMs) are aligned to a monolithic, often Western-centric set of values. This paper investigates the more challenging ta…
The Imperfect Learner: Incorporating Developmental Trajectories in Memory-based Student Simulation
Zhengyuan Liu, Stella Xin Yin, Bryan Chen Zhengyu Tan +5
User simulation is important for developing and evaluating human-centered AI, yet current student simulation in educational applications has significant limitations. Existing appro…
BLEnD-Vis: Benchmarking Multimodal Cultural Understanding in Vision Language Models
Bryan Chen Zhengyu Tan, Zheng Weihua, Zhengyuan Liu +4
As vision-language models (VLMs) are deployed globally, their ability to understand culturally situated knowledge becomes essential. Yet, existing evaluations largely assess static…
MMA-ASIA: A Multilingual and Multimodal Alignment Framework for Culturally-Grounded Evaluation
Weihua Zheng, Zhengyuan Liu, Tanmoy Chakraborty +32
Large language models (LLMs) are now used worldwide, yet their multimodal understanding and reasoning often degrade outside Western, high-resource settings. We propose MMA-ASIA, a…
Persuasion Dynamics in LLMs: Investigating Robustness and Adaptability in Knowledge and Safety with DuET-PD
Bryan Chen Zhengyu Tan, Daniel Wai Kit Chin, Zhengyuan Liu +2
Large Language Models (LLMs) can struggle to balance gullibility to misinformation and resistance to valid corrections in persuasive dialogues, a critical challenge for reliable de…