188 citations · 241 across the 8 of their papers we have counts for
8 papers
LLM-DA: Data Augmentation via Large Language Models for Few-Shot Named Entity Recognition
Junjie Ye, Nuo Xu, Yikun Wang +4
Despite the impressive capabilities of large language models (LLMs), their performance on information extraction tasks is still not entirely satisfactory. However, their remarkable…
Domain Generalization via Causal Adjustment for Cross-Domain Sentiment Analysis
Siyin Wang, Jie Zhou, Qin Chen +3
Domain adaption has been widely adapted for cross-domain sentiment analysis to transfer knowledge from the source domain to the target domain. Whereas, most methods are proposed un…
A Soft Contrastive Learning-based Prompt Model for Few-shot Sentiment Analysis
Jingyi Zhou, Jie Zhou, Jiabao Zhao +5
Few-shot text classification has attracted great interest in both academia and industry due to the lack of labeled data in many fields. Different from general text classification (…
RethinkingTMSC: An Empirical Study for Target-Oriented Multimodal Sentiment Classification
Junjie Ye, Jie Zhou, Junfeng Tian +4
Recently, Target-oriented Multimodal Sentiment Classification (TMSC) has gained significant attention among scholars. However, current multimodal models have reached a performance…
Farewell to Aimless Large-scale Pretraining: Influential Subset Selection for Language Model
Xiao Wang, Weikang Zhou, Qi Zhang +7
Pretrained language models have achieved remarkable success in various natural language processing tasks. However, pretraining has recently shifted toward larger models and larger…
A Confidence-based Partial Label Learning Model for Crowd-Annotated Named Entity Recognition
Limao Xiong, Jie Zhou, Qunxi Zhu +7
Existing models for named entity recognition (NER) are mainly based on large-scale labeled datasets, which always obtain using crowdsourcing. However, it is hard to obtain a unifie…
A Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series Models
Junjie Ye, Xuanting Chen, Nuo Xu +12
GPT series models, such as GPT-3, CodeX, InstructGPT, ChatGPT, and so on, have gained considerable attention due to their exceptional natural language processing capabilities. Howe…
How Robust is GPT-3.5 to Predecessors? A Comprehensive Study on Language Understanding Tasks
Xuanting Chen, Junjie Ye, Can Zu +7
The GPT-3.5 models have demonstrated impressive performance in various Natural Language Processing (NLP) tasks, showcasing their strong understanding and reasoning capabilities. Ho…