568 citations · 986 across the 37 of their papers we have counts for
37 papers
An Empirical Study on Information Extraction using Large Language Models
Ridong Han, Chaohao Yang, Tao Peng +4
Human-like large language models (LLMs), especially the most powerful and popular ones in OpenAI's GPT family, have proven to be very helpful for many natural language processing (…
AceGPT, Localizing Large Language Models in Arabic
Huang Huang, Fei Yu, Jianqing Zhu +17
This paper is devoted to the development of a localized Large Language Model (LLM) specifically for Arabic, a language imbued with unique cultural characteristics inadequately addr…
PlatoLM: Teaching LLMs in Multi-Round Dialogue via a User Simulator
Chuyi Kong, Yaxin Fan, Xiang Wan +2
The unparalleled performance of closed-sourced ChatGPT has sparked efforts towards its democratization, with notable strides made by leveraging real user and ChatGPT dialogues, as…
CMB: A Comprehensive Medical Benchmark in Chinese
Xidong Wang, Guiming Hardy Chen, Dingjie Song +8
Large Language Models (LLMs) provide a possibility to make a great breakthrough in medicine. The establishment of a standardized medical benchmark becomes a fundamental cornerstone…
On the Difference of BERT-style and CLIP-style Text Encoders
Zhihong Chen, Guiming Hardy Chen, Shizhe Diao +2
Masked language modeling (MLM) has been one of the most popular pretraining recipes in natural language processing, e.g., BERT, one of the representative models. Recently, contrast…
A Survey of Quantum-Cognitively Inspired Sentiment Analysis Models
Yaochen Liu, Qiuchi Li, Benyou Wang +2
Quantum theory, originally proposed as a physical theory to describe the motions of microscopic particles, has been applied to various non-physics domains involving human cognition…
HuatuoGPT, towards Taming Language Model to Be a Doctor
Hongbo Zhang, Junying Chen, Feng Jiang +10
In this paper, we present HuatuoGPT, a large language model (LLM) for medical consultation. The core recipe of HuatuoGPT is to leverage both \textit{distilled data from ChatGPT} an…
Injecting Knowledge into Biomedical Pre-trained Models via Polymorphism and Synonymous Substitution
Hongbo Zhang, Xiang Wan, Benyou Wang
Pre-trained language models (PLMs) were considered to be able to store relational knowledge present in the training data. However, some relational knowledge seems to be discarded u…
Lifting the Curse of Capacity Gap in Distilling Language Models
Chen Zhang, Yang Yang, Jiahao Liu +4
Pretrained language models (LMs) have shown compelling performance on various downstream tasks, but unfortunately they require a tremendous amount of inference compute. Knowledge d…
Word Grounded Graph Convolutional Network
Zhibin Lu, Qianqian Xie, Benyou Wang +1
Graph Convolutional Networks (GCNs) have shown strong performance in learning text representations for various tasks such as text classification, due to its expressive power in mod…
Huatuo-26M, a Large-scale Chinese Medical QA Dataset
Jianquan Li, Xidong Wang, Xiangbo Wu +6
In this paper, we release a largest ever medical Question Answering (QA) dataset with 26 million QA pairs. We benchmark many existing approaches in our dataset in terms of both ret…
Med-UniC: Unifying Cross-Lingual Medical Vision-Language Pre-Training by Diminishing Bias
Zhongwei Wan, Che Liu, Mi Zhang +6
The scarcity of data presents a critical obstacle to the efficacy of medical visionlanguage pre-training (VLP). A potential solution lies in the combination of datasets from variou…
An Empirical Study on Information Extraction using Large Language Models
Ridong Han, Chaohao Yang, Tao Peng +4
Human-like large language models (LLMs), especially the most powerful and popular ones in OpenAI's GPT family, have proven to be very helpful for many natural language processing (…
Phoenix: Democratizing ChatGPT across Languages
Zhihong Chen, Feng Jiang, Junying Chen +11
This paper presents our efforts to democratize ChatGPT across language. We release a large language model "Phoenix", achieving competitive performance among open-source English and…
Effective Open Intent Classification with K-center Contrastive Learning and Adjustable Decision Boundary
Xiaokang Liu, Jianquan Li, Jingjing Mu +3
Open intent classification, which aims to correctly classify the known intents into their corresponding classes while identifying the new unknown (open) intents, is an essential bu…
A Survey for Biomedical Text Summarization: From Pre-trained to Large Language Models
Qianqian Xie, Zheheng Luo, Benyou Wang +1
The exponential growth of biomedical texts such as biomedical literature and electronic health records (EHRs), poses a significant challenge for clinicians and researchers to acces…
Modular Retrieval for Generalization and Interpretation
Juhao Liang, Chen Zhang, Zhengyang Tang +3
New retrieval tasks have always been emerging, thus urging the development of new retrieval models. However, instantiating a retrieval model for each new retrieval task is resource…
Natural Language Reasoning, A Survey
Fei Yu, Hongbo Zhang, Prayag Tiwari +1
This survey paper proposes a clearer view of natural language reasoning in the field of Natural Language Processing (NLP), both conceptually and practically. Conceptually, we provi…
Adapting Pre-trained Language Models for Quantum Natural Language Processing
Qiuchi Li, Benyou Wang, Yudong Zhu +2
The emerging classical-quantum transfer learning paradigm has brought a decent performance to quantum computational models in many tasks, such as computer vision, by enabling a com…
Towards Unifying Medical Vision-and-Language Pre-training via Soft Prompts
Zhihong Chen, Shizhe Diao, Benyou Wang +2
Medical vision-and-language pre-training (Med-VLP) has shown promising improvements on many downstream medical tasks owing to its applicability to extracting generic representation…
Document-level Relation Extraction with Relation Correlations
Ridong Han, Tao Peng, Benyou Wang +2
Document-level relation extraction faces two overlooked challenges: long-tail problem and multi-label problem. Previous work focuses mainly on obtaining better contextual represent…
MorphTE: Injecting Morphology in Tensorized Embeddings
Guobing Gan, Peng Zhang, Sunzhu Li +2
In the era of deep learning, word embeddings are essential when dealing with text tasks. However, storing and accessing these embeddings requires a large amount of space. This is n…
Spatio-Temporal Contrastive Learning Enhanced GNNs for Session-based Recommendation
Zhongwei Wan, Xin Liu, Benyou Wang +5
Session-based recommendation (SBR) systems aim to utilize the user's short-term behavior sequence to predict the next item without the detailed user profile. Most recent works try…
DPTDR: Deep Prompt Tuning for Dense Passage Retrieval
Zhengyang Tang, Benyou Wang, Ting Yao
Deep prompt tuning (DPT) has gained great success in most natural language processing~(NLP) tasks. However, it is not well-investigated in dense retrieval where fine-tuning~(FT) st…