1 citations · 1 across the 10 of their papers we have counts for
10 papers
Personalization Meets Safety:Mechanisms,Risks,and Mitigations in Personalized LLMs
Yanyan Luo, Xue Han, Ruiqiao Bai +10
Large Language Models (LLMs) have enabled increasingly personalized interactions by adapting to users' preferences, contexts, and long-term histories. However, the mechanisms that…
R3S: Refining and Recovering Reinforcement Signals for Multilingual Understanding and Reasoning
Junxiao Liu, Zhijun Wang, Yixiao Li +6
Large reasoning models often default to English reasoning when processing non-English questions, yet their performance drops substantially when reasoning in the question language.…
PEGRL: Improving Machine Translation by Post-Editing Guided Reinforcement Learning
Yunzhi Shen, Hao Zhou, Xin Huang +3
Reinforcement learning (RL) has shown strong promise for LLM-based machine translation, with recent methods such as GRPO demonstrating notable gains; nevertheless, translation-orie…
Align to the Pivot: Dual Alignment with Self-Feedback for Multilingual Math Reasoning
Chunxu Zhao, Xin Huang, Xue Han +3
Despite the impressive reasoning abilities demonstrated by large language models (LLMs), empirical evidence indicates that they are not language agnostic as expected, leading to pe…
Investigating and Scaling up Code-Switching for Multilingual Language Model Pre-Training
Zhijun Wang, Jiahuan Li, Hao Zhou +7
Large language models (LLMs) exhibit remarkable multilingual capabilities despite the extreme language imbalance in the pre-training data. In this paper, we closely examine the rea…
Understanding LLMs' Cross-Lingual Context Retrieval: How Good It Is And Where It Comes From
Changjiang Gao, Hankun Lin, Xin Huang +5
Cross-lingual context retrieval (extracting contextual information in one language based on requests in another) is a fundamental aspect of cross-lingual alignment, but the perform…
R-PRM: Reasoning-Driven Process Reward Modeling
Shuaijie She, Junxiao Liu, Yifeng Liu +3
Large language models (LLMs) inevitably make mistakes when performing step-by-step mathematical reasoning. Process Reward Models (PRMs) have emerged as a promising solution by eval…
MoE-LPR: Multilingual Extension of Large Language Models through Mixture-of-Experts with Language Priors Routing
Hao Zhou, Zhijun Wang, Shujian Huang +6
Large Language Models (LLMs) are often English-centric due to the disproportionate distribution of languages in their pre-training data. Enhancing non-English language capabilities…
Getting More from Less: Large Language Models are Good Spontaneous Multilingual Learners
Shimao Zhang, Changjiang Gao, Wenhao Zhu +6
Recently, Large Language Models (LLMs) have shown impressive language capabilities. While most of the existing LLMs have very unbalanced performance across different languages, mul…
Large Language Models Are Cross-Lingual Knowledge-Free Reasoners
Peng Hu, Sizhe Liu, Changjiang Gao +5
Large Language Models have demonstrated impressive reasoning capabilities across multiple languages. However, the relationship between capabilities in different languages is less e…