Search Results for author: Chengyu Wang

Found 70 papers, 25 papers with code

UnClE: Explicitly Leveraging Semantic Similarity to Reduce the Parameters of Word Embeddings

no code implementations Findings (EMNLP) 2021 Zhi Li, Yuchen Zhai, Chengyu Wang, Minghui Qiu, Kailiang Li, Yin Zhang

Inspired by the fact that words with similar semantic can share a part of weights, we divide the embeddings of words into two parts: unique embedding and class embedding.

Language Modelling Semantic Similarity +2

Meta Distant Transfer Learning for Pre-trained Language Models

no code implementations EMNLP 2021 Chengyu Wang, Haojie Pan, Minghui Qiu, Jun Huang, Fei Yang, Yin Zhang

For tasks related to distant domains with different class label sets, PLMs may memorize non-transferable knowledge for the target domain and suffer from negative transfer.

Implicit Relations Meta-Learning +2

Distilling Instruction-following Abilities of Large Language Models with Task-aware Curriculum Planning

no code implementations22 May 2024 Yuanhao Yue, Chengyu Wang, Jun Huang, Peng Wang

The process of instruction tuning aligns pre-trained large language models (LLMs) with open-domain instructions and human-preferred responses.

Code Generation Instruction Following +1

Lifelong Knowledge Editing for LLMs with Retrieval-Augmented Continuous Prompt Learning

no code implementations6 May 2024 Qizhou Chen, Taolin Zhang, Xiaofeng He, Dongyang Li, Chengyu Wang, Longtao Huang, Hui Xue

Model editing aims to correct outdated or erroneous knowledge in large language models (LLMs) without the need for costly retraining.

knowledge editing Retrieval

R4: Reinforced Retriever-Reorder-Responder for Retrieval-Augmented Large Language Models

no code implementations4 May 2024 Taolin Zhang, Dongyang Li, Qizhou Chen, Chengyu Wang, Longtao Huang, Hui Xue, Xiaofeng He, Jun Huang

The reordering learning process is divided into two steps according to the quality of the generated responses: document order adjustment and document representation enhancement.

Graph Attention Hallucination +5

DWE+: Dual-Way Matching Enhanced Framework for Multimodal Entity Linking

2 code implementations7 Apr 2024 Shezheng Song, Shasha Li, Shan Zhao, Xiaopeng Li, Chengyu Wang, Jie Yu, Jun Ma, Tianwei Yan, Bin Ji, Xiaoguang Mao

Multimodal entity linking (MEL) aims to utilize multimodal information (usually textual and visual information) to link ambiguous mentions to unambiguous entities in knowledge base.

Contrastive Learning Entity Linking

DiffChat: Learning to Chat with Text-to-Image Synthesis Models for Interactive Image Creation

no code implementations8 Mar 2024 Jiapeng Wang, Chengyu Wang, Tingfeng Cao, Jun Huang, Lianwen Jin

We present DiffChat, a novel method to align Large Language Models (LLMs) to "chat" with prompt-as-input Text-to-Image Synthesis (TIS) models (e. g., Stable Diffusion) for interactive image creation.

Image Generation Instruction Following +1

Towards Understanding Cross and Self-Attention in Stable Diffusion for Text-Guided Image Editing

no code implementations6 Mar 2024 Bingyan Liu, Chengyu Wang, Tingfeng Cao, Kui Jia, Jun Huang

Deep Text-to-Image Synthesis (TIS) models such as Stable Diffusion have recently gained significant popularity for creative Text-to-image generation.

Denoising text-guided-image-editing

Do Large Language Models Understand Logic or Just Mimick Context?

no code implementations19 Feb 2024 Junbing Yan, Chengyu Wang, Jun Huang, Wei zhang

Over the past few years, the abilities of large language models (LLMs) have received extensive attention, which have performed exceptionally well in complicated scenarios such as logical reasoning and symbolic inference.

counterfactual In-Context Learning +1

A Dual-way Enhanced Framework from Text Matching Point of View for Multimodal Entity Linking

1 code implementation19 Dec 2023 Shezheng Song, Shan Zhao, Chengyu Wang, Tianwei Yan, Shasha Li, Xiaoguang Mao, Meng Wang

Multimodal Entity Linking (MEL) aims at linking ambiguous mentions with multimodal information to entity in Knowledge Graph (KG) such as Wikipedia, which plays a key role in many applications.

Entity Linking Text Matching

Towards Better Parameter-Efficient Fine-Tuning for Large Language Models: A Position Paper

no code implementations22 Nov 2023 Chengyu Wang, Junbing Yan, Wei zhang, Jun Huang

This paper delves into the pressing need in Parameter-Efficient Fine-Tuning (PEFT) for Large Language Models (LLMs).

Model Compression Position

From Complex to Simple: Unraveling the Cognitive Tree for Reasoning with Small Language Models

no code implementations12 Nov 2023 Junbing Yan, Chengyu Wang, Taolin Zhang, Xiaofeng He, Jun Huang, Wei zhang

Reasoning is a distinctive human capacity, enabling us to address complex problems by breaking them down into a series of manageable cognitive steps.

Language Modelling Logical Reasoning

Sharing, Teaching and Aligning: Knowledgeable Transfer Learning for Cross-Lingual Machine Reading Comprehension

no code implementations12 Nov 2023 Tingfeng Cao, Chengyu Wang, Chuanqi Tan, Jun Huang, Jinhui Zhu

In cross-lingual language understanding, machine translation is often utilized to enhance the transferability of models across languages, either by translating the training data from the source language to the target, or from the target to the source to aid inference.

Cross-Lingual Transfer Machine Reading Comprehension +2

Learning Knowledge-Enhanced Contextual Language Representations for Domain Natural Language Understanding

no code implementations12 Nov 2023 Ruyao Xu, Taolin Zhang, Chengyu Wang, Zhongjie Duan, Cen Chen, Minghui Qiu, Dawei Cheng, Xiaofeng He, Weining Qian

In the experiments, we evaluate KANGAROO over various knowledge-aware and general NLP tasks in both full and few-shot learning settings, outperforming various KEPLM training paradigms performance in closed-domains significantly.

Contrastive Learning Data Augmentation +4

BeautifulPrompt: Towards Automatic Prompt Engineering for Text-to-Image Synthesis

no code implementations12 Nov 2023 Tingfeng Cao, Chengyu Wang, Bingyan Liu, Ziheng Wu, Jinhui Zhu, Jun Huang

Then, to ensure that our generated prompts can generate more beautiful images, we further propose a Reinforcement Learning with Visual AI Feedback technique to fine-tune our model to maximize the reward values of the generated prompts, where the reward values are calculated based on the PickScore and the Aesthetic Scores.

Prompt Engineering Text-to-Image Generation

Uncertainty-aware Parameter-Efficient Self-training for Semi-supervised Language Understanding

1 code implementation19 Oct 2023 Jianing Wang, Qiushi Sun, Nuo Chen, Chengyu Wang, Jun Huang, Ming Gao, Xiang Li

The recent success of large pre-trained language models (PLMs) heavily hinges on massive labeled data, which typically produces inferior performance in low-resource scenarios.

Synslator: An Interactive Machine Translation Tool with Online Learning

no code implementations8 Oct 2023 Jiayi Wang, Ke Wang, Fengming Zhou, Chengyu Wang, Zhiyong Fu, Zeyu Feng, Yu Zhao, Yuqi Zhang

Interactive machine translation (IMT) has emerged as a progression of the computer-aided translation paradigm, where the machine translation system and the human translator collaborate to produce high-quality translations.

Language Modelling Machine Translation +1

Knowledgeable In-Context Tuning: Exploring and Exploiting Factual Knowledge for In-Context Learning

no code implementations26 Sep 2023 Jianing Wang, Chengyu Wang, Chuanqi Tan, Jun Huang, Ming Gao

Large language models (LLMs) enable in-context learning (ICL) by conditioning on a few labeled training examples as a text-based prompt, eliminating the need for parameter updates and achieving competitive performance.

Few-Shot Learning In-Context Learning +3

DualToken-ViT: Position-aware Efficient Vision Transformer with Dual Token Fusion

no code implementations21 Sep 2023 Zhenzhen Chu, Jiayu Chen, Cen Chen, Chengyu Wang, Ziheng Wu, Jun Huang, Weining Qian

Position-aware global tokens also contain the position information of the image, which makes our model better for vision tasks.

Image Classification object-detection +3

Making Small Language Models Better Multi-task Learners with Mixture-of-Task-Adapters

no code implementations20 Sep 2023 Yukang Xie, Chengyu Wang, Junbing Yan, Jiyong Zhou, Feiqi Deng, Jun Huang

Recently, Large Language Models (LLMs) have achieved amazing zero-shot learning performance over a variety of Natural Language Processing (NLP) tasks, especially for text generative tasks.

Zero-Shot Learning

PAI-Diffusion: Constructing and Serving a Family of Open Chinese Diffusion Models for Text-to-image Synthesis on the Cloud

no code implementations11 Sep 2023 Chengyu Wang, Zhongjie Duan, Bingyan Liu, Xinyi Zou, Cen Chen, Kui Jia, Jun Huang

Text-to-image synthesis for the Chinese language poses unique challenges due to its large vocabulary size, and intricate character relationships.

Image Generation Style Transfer

TransPrompt v2: A Transferable Prompting Framework for Cross-task Text Classification

no code implementations29 Aug 2023 Jianing Wang, Chengyu Wang, Cen Chen, Ming Gao, Jun Huang, Aoying Zhou

We propose TransPrompt v2, a novel transferable prompting framework for few-shot learning across similar or distant text classification tasks.

Few-Shot Learning Few-Shot Text Classification +1

On the Trustworthiness Landscape of State-of-the-art Generative Models: A Survey and Outlook

no code implementations31 Jul 2023 Mingyuan Fan, Chengyu Wang, Cen Chen, Yang Liu, Jun Huang

Diffusion models and large language models have emerged as leading-edge generative models, revolutionizing various aspects of human life.


On the Robustness of Split Learning against Adversarial Attacks

no code implementations16 Jul 2023 Mingyuan Fan, Cen Chen, Chengyu Wang, Wenmeng Zhou, Jun Huang

Split learning enables collaborative deep learning model training while preserving data privacy and model security by avoiding direct sharing of raw data and model details (i. e., sever and clients only hold partial sub-networks and exchange intermediate computations).

Adversarial Attack

ConaCLIP: Exploring Distillation of Fully-Connected Knowledge Interaction Graph for Lightweight Text-Image Retrieval

no code implementations28 May 2023 Jiapeng Wang, Chengyu Wang, Xiaodan Wang, Jun Huang, Lianwen Jin

Large-scale pre-trained text-image models with dual-encoder architectures (such as CLIP) are typically adopted for various vision-language applications, including text-image retrieval.

Image Retrieval Knowledge Distillation +2

Optimal Linear Subspace Search: Learning to Construct Fast and High-Quality Schedulers for Diffusion Models

1 code implementation24 May 2023 Zhongjie Duan, Chengyu Wang, Cen Chen, Jun Huang, Weining Qian

In this paper, we first provide a detailed theoretical and empirical analysis of the generation process of the diffusion models based on schedulers.

Image Generation

Towards Adaptive Prefix Tuning for Parameter-Efficient Language Model Fine-tuning

no code implementations24 May 2023 Zhen-Ru Zhang, Chuanqi Tan, Haiyang Xu, Chengyu Wang, Jun Huang, Songfang Huang

In addition, taking the gate as a probing, we validate the efficiency and effectiveness of the variable prefix.

Language Modelling NER

HugNLP: A Unified and Comprehensive Library for Natural Language Processing

2 code implementations28 Feb 2023 Jianing Wang, Nuo Chen, Qiushi Sun, Wenkang Huang, Chengyu Wang, Ming Gao

In this paper, we introduce HugNLP, a unified and comprehensive library for natural language processing (NLP) with the prevalent backend of HuggingFace Transformers, which is designed for NLP researchers to easily utilize off-the-shelf algorithms and develop novel methods with user-defined models and tasks in real-world scenarios.

Uncertainty-aware Self-training for Low-resource Neural Sequence Labeling

no code implementations17 Feb 2023 Jianing Wang, Chengyu Wang, Jun Huang, Ming Gao, Aoying Zhou

Neural sequence labeling (NSL) aims at assigning labels for input language tokens, which covers a broad range of applications, such as named entity recognition (NER) and slot filling, etc.

named-entity-recognition Named Entity Recognition +3

Refiner: Data Refining against Gradient Leakage Attacks in Federated Learning

no code implementations5 Dec 2022 Mingyuan Fan, Cen Chen, Chengyu Wang, Xiaodan Li, Wenmeng Zhou, Jun Huang

Recent works have brought attention to the vulnerability of Federated Learning (FL) systems to gradient leakage attacks.

Federated Learning Semantic Similarity +1

TW-BAG: Tensor-wise Brain-aware Gate Network for Inpainting Disrupted Diffusion Tensor Imaging

no code implementations31 Oct 2022 Zihao Tang, Xinyi Wang, Lihaowen Zhu, Mariano Cabezas, Dongnan Liu, Michael Barnett, Weidong Cai, Chengyu Wang

Diffusion Weighted Imaging (DWI) is an advanced imaging technique commonly used in neuroscience and neurological clinical research through a Diffusion Tensor Imaging (DTI) model.

Revisiting and Advancing Chinese Natural Language Understanding with Accelerated Heterogeneous Knowledge Pre-training

1 code implementation11 Oct 2022 Taolin Zhang, Junwei DOng, Jianing Wang, Chengyu Wang, Ang Wang, Yinghui Liu, Jun Huang, Yong Li, Xiaofeng He

Recently, knowledge-enhanced pre-trained language models (KEPLMs) improve context-aware representations via learning from structured relations in knowledge graphs, and/or linguistic knowledge from syntactic or dependency analysis.

Knowledge Graphs Language Modelling +2

Understanding Long Programming Languages with Structure-Aware Sparse Attention

1 code implementation27 May 2022 Tingting Liu, Chengyu Wang, Cen Chen, Ming Gao, Aoying Zhou

With top-$k$ sparse attention, the most crucial attention relation can be obtained with a lower computational cost.

Towards Unified Prompt Tuning for Few-shot Text Classification

1 code implementation11 May 2022 Jianing Wang, Chengyu Wang, Fuli Luo, Chuanqi Tan, Minghui Qiu, Fei Yang, Qiuhui Shi, Songfang Huang, Ming Gao

Prompt-based fine-tuning has boosted the performance of Pre-trained Language Models (PLMs) on few-shot text classification by employing task-specific prompts.

Few-Shot Learning Few-Shot Text Classification +4

KECP: Knowledge Enhanced Contrastive Prompting for Few-shot Extractive Question Answering

1 code implementation6 May 2022 Jianing Wang, Chengyu Wang, Minghui Qiu, Qiuhui Shi, Hongbin Wang, Jun Huang, Ming Gao

Extractive Question Answering (EQA) is one of the most important tasks in Machine Reading Comprehension (MRC), which can be solved by fine-tuning the span selecting heads of Pre-trained Language Models (PLMs).

Contrastive Learning Extractive Question-Answering +5

Making Pre-trained Language Models End-to-end Few-shot Learners with Contrastive Prompt Tuning

1 code implementation1 Apr 2022 Ziyun Xu, Chengyu Wang, Minghui Qiu, Fuli Luo, Runxin Xu, Songfang Huang, Jun Huang

Pre-trained Language Models (PLMs) have achieved remarkable performance for various language understanding tasks in IR systems, which require the fine-tuning process based on labeled training data.

Contrastive Learning

HiCLRE: A Hierarchical Contrastive Learning Framework for Distantly Supervised Relation Extraction

1 code implementation Findings (ACL) 2022 Dongyang Li, Taolin Zhang, Nan Hu, Chengyu Wang, Xiaofeng He

In this paper, we propose a hierarchical contrastive learning Framework for Distantly Supervised relation extraction (HiCLRE) to reduce noisy sentences, which integrate the global structural information and local fine-grained interaction.

Contrastive Learning Data Augmentation +3

From Dense to Sparse: Contrastive Pruning for Better Pre-trained Language Model Compression

2 code implementations14 Dec 2021 Runxin Xu, Fuli Luo, Chengyu Wang, Baobao Chang, Jun Huang, Songfang Huang, Fei Huang

Unified in contrastive learning, CAP enables the pruned model to learn from the pre-trained model for task-agnostic knowledge, and fine-tuned model for task-specific knowledge.

Contrastive Learning Language Modelling +2

DKPLM: Decomposable Knowledge-enhanced Pre-trained Language Model for Natural Language Understanding

1 code implementation2 Dec 2021 Taolin Zhang, Chengyu Wang, Nan Hu, Minghui Qiu, Chengguang Tang, Xiaofeng He, Jun Huang

Knowledge-Enhanced Pre-trained Language Models (KEPLMs) are pre-trained models with relation triples injecting from knowledge graphs to improve language understanding abilities.

Knowledge Graphs Knowledge Probing +3

INTERN: A New Learning Paradigm Towards General Vision

no code implementations16 Nov 2021 Jing Shao, Siyu Chen, Yangguang Li, Kun Wang, Zhenfei Yin, Yinan He, Jianing Teng, Qinghong Sun, Mengya Gao, Jihao Liu, Gengshi Huang, Guanglu Song, Yichao Wu, Yuming Huang, Fenggang Liu, Huan Peng, Shuo Qin, Chengyu Wang, Yujie Wang, Conghui He, Ding Liang, Yu Liu, Fengwei Yu, Junjie Yan, Dahua Lin, Xiaogang Wang, Yu Qiao

Enormous waves of technological innovations over the past several years, marked by the advances in AI technologies, are profoundly reshaping the industry and the society.

Snapshot Ptychography on Array cameras

1 code implementation5 Nov 2021 Chengyu Wang, Minghao Hu, Yuzuru Takashima, Timothy J. Schulz, David J. Brady

We use convolutional neural networks to recover images optically down-sampled by $6. 7\times$ using coherent aperture synthesis over a 16 camera array.

Path-Enhanced Multi-Relational Question Answering with Knowledge Graph Embeddings

no code implementations29 Oct 2021 Guanglin Niu, Yang Li, Chengguang Tang, Zhongkai Hu, Shibin Yang, Peng Li, Chengyu Wang, Hao Wang, Jian Sun

The multi-relational Knowledge Base Question Answering (KBQA) system performs multi-hop reasoning over the knowledge graph (KG) to achieve the answer.

Knowledge Base Question Answering Knowledge Graph Embedding +1

SMedBERT: A Knowledge-Enhanced Pre-trained Language Model with Structured Semantics for Medical Text Mining

2 code implementations ACL 2021 Taolin Zhang, Zerui Cai, Chengyu Wang, Minghui Qiu, Bite Yang, Xiaofeng He

Recently, the performance of Pre-trained Language Models (PLMs) has been significantly improved by injecting knowledge facts to enhance their abilities of language understanding.

Language Modelling Natural Language Inference +1

Multiscale Phase Retrieval

1 code implementation9 Dec 2020 David J. Brady, Timothy J. Schulz, Chengyu Wang

Phase-sensitive sensor planes using such devices could eliminate the need both for lenses and reference signals, creating a path to large aperture diffraction limited laser imaging.


Learning to Expand: Reinforced Pseudo-relevance Feedback Selection for Information-seeking Conversations

no code implementations25 Nov 2020 Haojie Pan, Cen Chen, Chengyu Wang, Minghui Qiu, Liu Yang, Feng Ji, Jun Huang

More specifically, we propose a reinforced selector to extract useful PRF terms to enhance response candidates and a BERT-based response ranker to rank the PRF-enhanced responses.

EasyTransfer -- A Simple and Scalable Deep Transfer Learning Platform for NLP Applications

2 code implementations18 Nov 2020 Minghui Qiu, Peng Li, Chengyu Wang, Hanjie Pan, Ang Wang, Cen Chen, Xianyan Jia, Yaliang Li, Jun Huang, Deng Cai, Wei Lin

The literature has witnessed the success of leveraging Pre-trained Language Models (PLMs) and Transfer Learning (TL) algorithms to a wide range of Natural Language Processing (NLP) applications, yet it is not easy to build an easy-to-use and scalable TL toolkit for this purpose.

Compiler Optimization Conversational Question Answering +1

EasyASR: A Distributed Machine Learning Platform for End-to-end Automatic Speech Recognition

no code implementations14 Sep 2020 Chengyu Wang, Mengli Cheng, Xu Hu, Jun Huang

We present EasyASR, a distributed machine learning platform for training and serving large-scale Automatic Speech Recognition (ASR) models, as well as collecting and processing audio data at scale.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

Knowledge-Empowered Representation Learning for Chinese Medical Reading Comprehension: Task, Model and Resources

1 code implementation Findings (ACL) 2021 Taolin Zhang, Chengyu Wang, Minghui Qiu, Bite Yang, Xiaofeng He, Jun Huang

In this paper, we introduce a multi-target MRC task for the medical domain, whose goal is to predict answers to medical questions and the corresponding support sentences from medical information sources simultaneously, in order to ensure the high reliability of medical knowledge serving.

Machine Reading Comprehension Multi-Task Learning +1

Weakly Supervised Construction of ASR Systems with Massive Video Data

no code implementations4 Aug 2020 Mengli Cheng, Chengyu Wang, Xu Hu, Jun Huang, Xiaobo Wang

Building Automatic Speech Recognition (ASR) systems from scratch is significantly challenging, mostly due to the time-consuming and financially-expensive process of annotating a large amount of audio data with transcripts.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +4

Meta Fine-Tuning Neural Language Models for Multi-Domain Text Mining

2 code implementations EMNLP 2020 Chengyu Wang, Minghui Qiu, Jun Huang, Xiaofeng He

In this paper, we propose an effective learning procedure named Meta Fine-Tuning (MFT), served as a meta-learner to solve a group of similar NLP tasks for neural language models.

Few-Shot Learning Language Modelling

KEML: A Knowledge-Enriched Meta-Learning Framework for Lexical Relation Classification

no code implementations25 Feb 2020 Chengyu Wang, Minghui Qiu, Jun Huang, Xiaofeng He

We further combine a meta-learning process over the auxiliary task distribution and supervised learning to train the neural lexical relation classifier.

General Classification Meta-Learning +2

Population pharmacokinetics and dosing regimen optimization of tacrolimus in Chinese lung transplant recipients

no code implementations1 Feb 2020 Xiaojun Cai, Huizhu Song, Zheng Jiao, Hang Yang, Min Zhu, Chengyu Wang, Dong Wei, Lingzhi Shi, Bo Wu, Jinyu Chen

Given the nonlinear kinetics of tacrolimus and large variability, population pharmacokinetic model should be combined with therapeutic drug monitoring to optimize individualized therapy.

Video Generation from Single Semantic Label Map

2 code implementations CVPR 2019 Junting Pan, Chengyu Wang, Xu Jia, Jing Shao, Lu Sheng, Junjie Yan, Xiaogang Wang

This paper proposes the novel task of video generation conditioned on a SINGLE semantic label map, which provides a good balance between flexibility and quality in the generation process.

Image Generation Image to Video Generation +1

Learning Fine-grained Relations from Chinese User Generated Categories

no code implementations EMNLP 2017 Chengyu Wang, Yan Fan, Xiaofeng He, Aoying Zhou

User generated categories (UGCs) are short texts that reflect how people describe and organize entities, expressing rich semantic relations implicitly.

Graph Mining Relation Extraction +1

Transductive Non-linear Learning for Chinese Hypernym Prediction

no code implementations ACL 2017 Chengyu Wang, Junchi Yan, Aoying Zhou, Xiaofeng He

Finding the correct hypernyms for entities is essential for taxonomy learning, fine-grained entity categorization, query understanding, etc.

Relation Extraction Transductive Learning

Chinese Hypernym-Hyponym Extraction from User Generated Categories

no code implementations COLING 2016 Chengyu Wang, Xiaofeng He

Hypernym-hyponym ({``}is-a{''}) relations are key components in taxonomies, object hierarchies and knowledge graphs.

Knowledge Graphs Machine Translation +5

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