3 citations · 3 across the 7 of their papers we have counts for
9 papers
Drift-Constrained Optimization: Only Direction Matters in Fine-Tuning Instruct Models
Fei Yuan, Changjiang Gao, Yilei Tu +3
Fine-tuning instruct models often improves target performance while inducing behavioral drift from the reference model, which can degrade existing capabilities. Rather than treatin…
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…
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.…
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…
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…
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…
MindMerger: Efficient Boosting LLM Reasoning in non-English Languages
Zixian Huang, Wenhao Zhu, Gong Cheng +2
Reasoning capabilities are crucial for Large Language Models (LLMs), yet a notable gap exists between English and non-English languages. To bridge this disparity, some works fine-t…
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…
Question Translation Training for Better Multilingual Reasoning
Wenhao Zhu, Shujian Huang, Fei Yuan +3
Large language models show compelling performance on reasoning tasks but they tend to perform much worse in languages other than English. This is unsurprising given that their trai…