3 citations · 8 across the 6 of their papers we have counts for
6 papers
Unsupervised Distractor Generation via Large Language Model Distilling and Counterfactual Contrastive Decoding
Fanyi Qu, Hao Sun, Yunfang Wu
Within the context of reading comprehension, the task of Distractor Generation (DG) aims to generate several incorrect options to confuse readers. Traditional supervised methods fo…
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…
Evaluating the Capability of Large-scale Language Models on Chinese Grammatical Error Correction Task
Fanyi Qu, Chenming Tang, Yunfang Wu
Large-scale language models (LLMs) has shown remarkable capability in various of Natural Language Processing (NLP) tasks and attracted lots of attention recently. However, some stu…
Enhancing Pre-trained Models with Text Structure Knowledge for Question Generation
Zichen Wu, Xin Jia, Fanyi Qu +1
Today the pre-trained language models achieve great success for question generation (QG) task and significantly outperform traditional sequence-to-sequence approaches. However, the…
Asking Questions Like Educational Experts: Automatically Generating Question-Answer Pairs on Real-World Examination Data
Fanyi Qu, Xin Jia, Yunfang Wu
Generating high quality question-answer pairs is a hard but meaningful task. Although previous works have achieved great results on answer-aware question generation, it is difficul…