3 citations · 4 across the 7 of their papers we have counts for
7 papers
SCOI: Syntax-augmented Coverage-based In-context Example Selection for Machine Translation
Chenming Tang, Zhixiang Wang, Yunfang Wu
In-context learning (ICL) greatly improves the performance of large language models (LLMs) on various down-stream tasks, where the improvement highly depends on the quality of demo…
FPT: Feature Prompt Tuning for Few-shot Readability Assessment
Ziyang Wang, Sanwoo Lee, Hsiu-Yuan Huang +1
Prompt-based methods have achieved promising results in most few-shot text classification tasks. However, for readability assessment tasks, traditional prompt methods lackcrucial l…
Ungrammatical-syntax-based In-context Example Selection for Grammatical Error Correction
Chenming Tang, Fanyi Qu, Yunfang Wu
In the era of large language models (LLMs), in-context learning (ICL) stands out as an effective prompting strategy that explores LLMs' potency across various tasks. However, apply…
Mixture-of-Prompt-Experts for Multi-modal Semantic Understanding
Zichen Wu, Hsiu-Yuan Huang, Fanyi Qu +1
Deep multimodal semantic understanding that goes beyond the mere superficial content relation mining has received increasing attention in the realm of artificial intelligence. The…
Multi-modal Semantic Understanding with Contrastive Cross-modal Feature Alignment
Ming Zhang, Ke Chang, Yunfang Wu
Multi-modal semantic understanding requires integrating information from different modalities to extract users' real intention behind words. Most previous work applies a dual-encod…
Are Pre-trained Language Models Useful for Model Ensemble in Chinese Grammatical Error Correction?
Chenming Tang, Xiuyu Wu, Yunfang Wu
Model ensemble has been in widespread use for Grammatical Error Correction (GEC), boosting model performance. We hypothesize that model ensemble based on the perplexity (PPL) compu…