Dun Li Chan

How Perturbations Propagate: A Multi-Level Analysis of Robustness in Large Language Models

Dun Li Chan, Emily Liu, Niyathi Allu, Christian Hoang

Language models encounter typos, corrupted text, altered words, and disrupted token order, yet robustness is usually evaluated only through output behavior. We study how six naturalistic and synthetic input perturbations propagate through decoder-only language models at three levels: output behavior, hidden-state geometry, …

Anthropogenic Regional Adaptation in Multimodal Vision-Language Model

Samuel Cahyawijaya, Peerat Limkonchotiwat, Tack Hwa Wong, Hitesh Laxmichand Patel et autres

While the field of vision-language (VL) has achieved remarkable success in integrating visual and textual information across multiple languages and domains, there is still no dedicated framework for assessing human-centric alignment in vision-language systems. We offer two contributions to address this gap. …

Anthropogenic Regional Adaptation in Multimodal Vision-Language Model

Samuel Cahyawijaya, Peerat Limkonchotiwat, Tack Hwa Wong, Hitesh Laxmichand Patel et autres

While the field of vision-language (VL) has achieved remarkable success in integrating visual and textual information across multiple languages and domains, there is still no dedicated framework for assessing human-centric alignment in vision-language systems. We offer two contributions to address this gap. …

th, ca, my, us, au, ph, gb, sg, id, ae (code pays fourni par la source)

Can Linguistically Related Languages Guide LLM Translation in Low-Resource Settings?

Aishwarya Ramasethu, Niyathi Allu, Rohin Garg, Harshwardhan Fartale et autres

Large Language Models (LLMs) have achieved strong performance across many downstream tasks, yet their effectiveness in extremely low-resource machine translation remains limited. Standard adaptation techniques typically rely on large-scale parallel data or extensive fine-tuning, which are infeasible for the long tail of …

Can Linguistically Related Languages Guide LLM Translation in Low-Resource Settings?

Aishwarya Ramasethu, Niyathi Allu, Rohin Garg, Harshwardhan Fartale et autres

Large Language Models (LLMs) have achieved strong performance across many downstream tasks, yet their effectiveness in extremely low-resource machine translation remains limited. Standard adaptation techniques typically rely on large-scale parallel data or extensive fine-tuning, which are infeasible for the long tail of …

my, us (code pays fourni par la source)

Do All LLMs Know When They're Being Harmful? A Reproducibility Study of Latent-Space Safety Probes Across Model Families

Alizishaan Khatri, Dun Li Chan

my, us (code pays fourni par la source)