Ho Yin Sam Ng — accepted AI-conference papers

2026

ImagenWorld: Stress-Testing Image Generation Models with Explainable Human Evaluation on Open-ended Real-World Tasks

Samin Mahdizadeh Sani, Max Ku, Nima Jamali, Matina Mahdizadeh Sani, Paria Khoshtab, Wei-Chieh Sun +20

ICLR 2026poster

Advances in diffusion, autoregressive, and hybrid models have enabled high-quality image synthesis for tasks such as text-to-image, editing, and reference-guided composition. Yet, existing benchmarks remain limited, either focus on isolated tasks, cover only narrow domains, or provide opaque scores…

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2025

LaMP-Cap: Personalized Figure Caption Generation With Multimodal Figure Profiles

Ho Yin Sam Ng, Edward Hsu, Aashish Anantha Ramakrishnan, Branislav Kveton, Nedim Lipka, Franck Dernoncourt +5

EMNLP 2025

Figure captions are crucial for helping readers understand and remember a figure’s key message. Many models have been developed to generate these captions, helping authors compose better quality captions more easily. Yet, authors almost always need to revise generic AI-generated captions to match th

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2025

Using Contextually Aligned Online Reviews to Measure LLMs’ Performance Disparities Across Language Varieties

Zixin Tang, Chieh-Yang Huang, Tsung-che Li, Ho Yin Sam Ng, Hen-Hsen Huang, Ting-Hao Kenneth Huang

NAACL 2025short

A language can have different varieties. These varieties can affect the performance of natural language processing (NLP) models, including large language models (LLMs), which are often trained on data from widely spoken varieties. This paper introduces a novel and cost-effective approach to benchmar…

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