3 papers
cs.CL2025
FEANEL: A Benchmark for Fine-Grained Error Analysis in K-12 English Writing
Jingheng Ye, Shen Wang, Jiaqi Chen +9
Large Language Models (LLMs) have transformed artificial intelligence, offering profound opportunities for educational applications. However, their ability to provide fine-grained…
cs.IR2025
LexSemBridge: Fine-Grained Dense Representation Enhancement through Token-Aware Embedding Augmentation
Shaoxiong Zhan, Hai Lin, Hongming Tan +6
As queries in retrieval-augmented generation (RAG) pipelines powered by large language models (LLMs) become increasingly complex and diverse, dense retrieval models have demonstrat…
cs.CL2025
CLEME2.0: Towards Interpretable Evaluation by Disentangling Edits for Grammatical Error Correction
Jingheng Ye, Zishan Xu, Yinghui Li +9
The paper focuses on the interpretability of Grammatical Error Correction (GEC) evaluation metrics, which received little attention in previous studies. To bridge the gap, we intro…