5 papers
MC-PDD: Masked Corpus-Level Pretraining Data Detection for Black-Box Large Language Models
Kaixin Lan, Mu You, Tao Fang +3
Pretraining is fundamental to the development of Large Language Models (LLMs), yet the opacity of pretraining data complicates model analysis and raises ethical, legal, and fairnes…
Worlds Within Words: Translating Culture in Ancient Chinese Texts with Multi-Agent Coordination
Xiaoqi He, Kaixin Lan, Mu You +3
Large language model (LLM)-based machine translation has advanced cross-cultural communication, yet it still struggles with culture-loaded words (CLWs) in ancient Chinese texts. Th…
CLIF: Concept-Level Influence Functions for Transparent Bottleneck Models
Yike Sun, Mingkun Xu, Mu You +5
In recent years, the black-box nature of deep learning models has limited their application in high-stakes domains such as medical diagnosis and finance, where interpretability is…
LLMCL-GEC: Advancing Grammatical Error Correction with LLM-Driven Curriculum Learning
Tao Fang, Derek F. Wong, Lusheng Zhang +5
While large-scale language models (LLMs) have demonstrated remarkable capabilities in specific natural language processing (NLP) tasks, they may still lack proficiency compared to…
FOCUS: Forging Originality through Contrastive Use in Self-Plagiarism for Language Models
Kaixin Lan, Tao Fang, Derek F. Wong +3
Pre-trained Language Models (PLMs) have shown impressive results in various Natural Language Generation (NLG) tasks, such as powering chatbots and generating stories. However, an e…