Multilingual Large Language Models are capable of using powerful Large Language
Models to handle and respond to queries in multiple languages, which achieves remarkable …

Despite the success of existing instruction-tuned models, we find that they usually struggle to
respond to queries with multiple instructions. This impairs their performance in complex …

Multilingual large language models (MLLMs) leverage advanced large language models to
process and respond to queries across multiple languages, achieving significant success in …

This paper investigates crosslingual content scoring, a scenario where scoring models
trained on learner data in one language are applied to data in a different language. We …

Pre-trained multilingual language models have enabled significant advancements in cross-
lingual transfer. However, these models often exhibit a performance disparity when …

Text generation has become more accessible than ever, and the increasing interest in these
systems, especially those using large language models, has spurred an increasing number …

Abstract While Large Language Models (LLMs) have demonstrated considerable potential
in advancing natural language processing in dialect-specific contexts, their effectiveness in …

The brittleness of finetuned language model performance on out-of-distribution (OOD) test
samples in unseen domains has been well-studied for English, yet is unexplored for multi …

Zero-shot cross-lingual transfer by fine-tuning multilingual pretrained models shows promise
for low-resource languages, but often suffers from misalignment of internal representations …

Building a reliable visual question answering~(VQA) system across different languages is a
challenging problem, primarily due to the lack of abundant samples for training. To address …