2 citations · 3 across the 14 of their papers we have counts for
6 papers · 2 filters
Do Large Language Models Truly Understand Cross-cultural Differences?
Shiwei Guo, Sihang Jiang, Qianxi He +6
In recent years, large language models (LLMs) have demonstrated strong performance on multilingual tasks. Given its wide range of applications, cross-cultural understanding capabil…
CultureScope: A Dimensional Lens for Probing Cultural Understanding in LLMs
Jinghao Zhang, Sihang Jiang, Shiwei Guo +7
As large language models (LLMs) are increasingly deployed in diverse cultural environments, evaluating their cultural understanding capability has become essential for ensuring tru…
M-DaQ: Retrieving Samples with Multilingual Diversity and Quality for Instruction Fine-Tuning Datasets
Chunguang Zhao, Yilun Liu, Pufan Zeng +10
Multilingual instruction fine-tuning (IFT) empowers large language models to generalize across diverse linguistic and cultural contexts; however, high-quality, systematically curat…
ELSPR: Evaluator LLM Training Data Self-Purification on Non-Transitive Preferences via Tournament Graph Reconstruction
Yan Yu, Yilun Liu, Minggui He +9
Pairwise evaluation of large language models (LLMs) has become the dominant paradigm for benchmarking open-ended tasks, yet non-transitive preferences, where evaluators prefer A ov…
MIDB: Multilingual Instruction Data Booster for Enhancing Cultural Equality in Multilingual Instruction Synthesis
Yilun Liu, Chunguang Zhao, Xinhua Yang +9
Despite doubts on data quality, instruction synthesis has been widely applied into instruction tuning (IT) of LLMs as an economic and rapid alternative. Recent endeavors focus on i…
R1-T1: Fully Incentivizing Translation Capability in LLMs via Reasoning Learning
Minggui He, Yilun Liu, Shimin Tao +10
Despite recent breakthroughs in reasoning-enhanced large language models (LLMs) like DeepSeek-R1, incorporating inference-time reasoning into machine translation (MT), where human…