activity
20242026
collaborators

11 papers

cs.CL2026

Too Open for Opinion? Embracing Open-Endedness in Large Language Models for Social Simulation

Bolei Ma, Yong Cao, Indira Sen +4

Large Language Models (LLMs) are increasingly used to simulate public opinion and other social phenomena. Most current studies constrain these simulations to multiple-choice or sho…

cs.CV2026

RAVENEA: A Benchmark for Multimodal Retrieval-Augmented Visual Culture Understanding

Jiaang Li, Yifei Yuan, Wenyan Li +8

As vision-language models (VLMs) become increasingly integrated into daily life, the need for accurate visual culture understanding is becoming critical. Yet, these models frequent…

cs.CY2025

Cross-cultural value alignment frameworks for responsible AI governance: Evidence from China-West comparative analysis

Haijiang Liu, Jinguang Gu, Xun Wu +2

As Large Language Models (LLMs) increasingly influence high-stakes decision-making across global contexts, ensuring their alignment with diverse cultural values has become a critic…

cs.CL2025

EvalCards: A Framework for Standardized Evaluation Reporting

Ruchira Dhar, Danae Sanchez Villegas, Antonia Karamolegkou +11

Evaluation has long been a central concern in NLP, and transparent reporting practices are more critical than ever in today's landscape of rapidly released open-access models. Draw…

cs.CL2025

Beyond Demographics: Enhancing Cultural Value Survey Simulation with Multi-Stage Personality-Driven Cognitive Reasoning

Haijiang Liu, Qiyuan Li, Chao Gao +5

Introducing MARK, the Multi-stAge Reasoning frameworK for cultural value survey response simulation, designed to enhance the accuracy, steerability, and interpretability of large l…

cs.HC2025

Evaluating Multimodal Language Models as Visual Assistants for Visually Impaired Users

Antonia Karamolegkou, Malvina Nikandrou, Georgios Pantazopoulos +5

This paper explores the effectiveness of Multimodal Large Language models (MLLMs) as assistive technologies for visually impaired individuals. We conduct a user survey to identify…