6 papers
A Controllable Examination for Long-Context Language Models
Yijun Yang, Zeyu Huang, Wenhao Zhu +4
Existing frameworks for evaluating long-context language models (LCLM) can be broadly categorized into real-world applications (e.g, document summarization) and synthetic tasks (e.…
LLaMAX2: Your Translation-Enhanced Model also Performs Well in Reasoning
Changjiang Gao, Zixian Huang, Jingyang Gong +3
General Large Language Models (LLMs) excel in reasoning, but those enhanced for translation struggle with reasoning tasks. To address this, we propose a novel translationenhanced r…
BenchMAX: A Comprehensive Multilingual Evaluation Suite for Large Language Models
Xu Huang, Wenhao Zhu, Hanxu Hu +4
Previous multilingual benchmarks focus primarily on simple understanding tasks, but for large language models(LLMs), we emphasize proficiency in instruction following, reasoning, l…
Could Thinking Multilingually Empower LLM Reasoning?
Changjiang Gao, Xu Huang, Wenhao Zhu +3
Previous work indicates that large language models exhibit a significant "English bias", i.e. they often perform better when tasks are presented in English. Interestingly, we have…
The Power of Question Translation Training in Multilingual Reasoning: Broadened Scope and Deepened Insights
Wenhao Zhu, Shujian Huang, Fei Yuan +3
Bridging the significant gap between large language model's English and non-English performance presents a great challenge. While some previous studies attempt to mitigate this gap…
LLaMAX: Scaling Linguistic Horizons of LLM by Enhancing Translation Capabilities Beyond 100 Languages
Yinquan Lu, Wenhao Zhu, Lei Li +2
Large Language Models (LLMs) demonstrate remarkable translation capabilities in high-resource language tasks, yet their performance in low-resource languages is hindered by insuffi…