15 papers
Do Large Language Models Perform Well on Comprehending Poetic Logic in Modern Chinese Poetry?
Tian Lan, Shanshan Wang, Zehua Duo +4
Large Language Models (LLMs) have achieved significant progress across a wide range of natural language processing (NLP) tasks, yet their ability to understand literary texts, part…
Poller: Are LLMs Suitable for Evaluating the Poetry Understanding Task?
Shanshan Wang, Derek F. Wong, Jingming Yao +1
Traditional automatic evaluation methods have been shown to be unsuitable for modern Chinese poetry because of the distinct nature of this literary genre. Human evaluation remains…
Privacy-Preserving RAG via Multi-Agent Semantic Rewriting: Achieving Confidentiality Without Compromising Contextual Fidelity
Yuanhe Zhao, Tianyu Zhang, Huafei Xing +3
Retrieval-Augmented Generation enhances large language models by incorporating external knowledge, but deploying it in sensitive scenarios risks privacy leakage via malicious promp…
From Texts to Scores: Tracing the Emergence of Essay Quality Representations in Large Language Models
Jiaxu Zuo, Mu You, Kaixin Lan +5
Recent advances in Large Language Models (LLMs) have substantially transformed Automated Essay Scoring (AES), yet the internal mechanisms underlying LLM-based scoring remain poorly…
G-IdiomAlign: A Gloss-Pivoted Benchmark for Cross-Lingual Idiom Alignment
Fengying Ye, Yanming Sun, Runzhe Zhan +3
Idioms are difficult to transfer across languages due to their non-compositionality and weak surface-form grounding, making literal mappings unreliable. We present G-IdiomAlign, a…
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…