Wangchunshu Zhou

research

∙ 09/16/2023

Struc-Bench: Are Large Language Models Really Good at Generating Complex Structured Data?

Despite the power of Large Language Models (LLMs) like GPT-4, they still...

0 Xiangru Tang, et al. ∙

research

∙ 05/24/2023

SmartTrim: Adaptive Tokens and Parameters Pruning for Efficient Vision-Language Models

Despite achieving remarkable performance on various vision-language task...

0 Zekun Wang, et al. ∙

research

∙ 05/22/2023

RecurrentGPT: Interactive Generation of (Arbitrarily) Long Text

The fixed-size context of Transformer makes GPT models incapable of gene...

0 Wangchunshu Zhou, et al. ∙

research

∙ 05/22/2023

Interactive Natural Language Processing

Interactive Natural Language Processing (iNLP) has emerged as a novel pa...

3 Zekun Wang, et al. ∙

research

∙ 05/18/2023

Efficient Prompting via Dynamic In-Context Learning

The primary way of building AI applications is shifting from training sp...

0 Wangchunshu Zhou, et al. ∙

research

∙ 04/27/2023

Controlled Text Generation with Natural Language Instructions

Large language models generate fluent texts and can follow natural langu...

0 Wangchunshu Zhou, et al. ∙

research

∙ 01/21/2023

Poor Man's Quality Estimation: Predicting Reference-Based MT Metrics Without the Reference

Machine translation quality estimation (QE) predicts human judgements of...

0 Vilém Zouhar, et al. ∙

research

∙ 11/22/2022

X^2-VLM: All-In-One Pre-trained Model For Vision-Language Tasks

Vision language pre-training aims to learn alignments between vision and...

0 Yan Zeng, et al. ∙

research

∙ 10/21/2022

Efficiently Tuned Parameters are Task Embeddings

Intermediate-task transfer can benefit a wide range of NLP tasks with pr...

4 Wangchunshu Zhou, et al. ∙

research

∙ 10/14/2022

EfficientVLM: Fast and Accurate Vision-Language Models via Knowledge Distillation and Modal-adaptive Pruning

Pre-trained vision-language models (VLMs) have achieved impressive resul...

4 Tiannan Wang, et al. ∙

research

∙ 06/15/2022

Prefix Language Models are Unified Modal Learners

With the success of vision-language pre-training, we have witnessed the ...

21 Shizhe Diao, et al. ∙

research

∙ 06/01/2022

Cross-View Language Modeling: Towards Unified Cross-Lingual Cross-Modal Pre-training

In this paper, we introduce Cross-View Language Modeling, a simple and e...

0 Yan Zeng, et al. ∙

research

∙ 05/30/2022

VLUE: A Multi-Task Benchmark for Evaluating Vision-Language Models

Recent advances in vision-language pre-training (VLP) have demonstrated ...

0 Wangchunshu Zhou, et al. ∙

research

∙ 04/08/2022

Contextual Representation Learning beyond Masked Language Modeling

How do masked language models (MLMs) such as BERT learn contextual repre...

0 Zhiyi Fu, et al. ∙

research

∙ 11/30/2021

Learning to Predict Persona Information forDialogue Personalization without Explicit Persona Description

Personalizing dialogue agents is important for dialogue systems to gener...

0 Wangchunshu Zhou, et al. ∙

research

∙ 11/08/2021

A Survey on Green Deep Learning

In recent years, larger and deeper models are springing up and continuou...

0 Jingjing Xu, et al. ∙

research

∙ 09/07/2021

Beyond Preserved Accuracy: Evaluating Loyalty and Robustness of BERT Compression

Recent studies on compression of pretrained language models (e.g., BERT)...

0 Canwen Xu, et al. ∙

research

∙ 06/08/2021

Meta Learning for Knowledge Distillation

We present Meta Learning for Knowledge Distillation (MetaDistil), a simp...

0 Wangchunshu Zhou, et al. ∙

research

∙ 05/31/2021

Learning from Perturbations: Diverse and Informative Dialogue Generation with Inverse Adversarial Training

In this paper, we propose Inverse Adversarial Training (IAT) algorithm f...

0 Wangchunshu Zhou, et al. ∙

research

∙ 04/06/2021

Blow the Dog Whistle: A Chinese Dataset for Cant Understanding with Common Sense and World Knowledge

Cant is important for understanding advertising, comedies and dog-whistl...

0 Canwen Xu, et al. ∙

research

∙ 01/02/2021

Improving Sequence-to-Sequence Pre-training via Sequence Span Rewriting

In this paper, we generalize text infilling (e.g., masked language model...

0 Wangchunshu Zhou, et al. ∙

research

∙ 10/11/2020

Connecting the Dots Between Fact Verification and Fake News Detection

Fact verification models have enjoyed a fast advancement in the last two...

0 Qifei Li, et al. ∙

research

∙ 06/07/2020

BERT Loses Patience: Fast and Robust Inference with Early Exit

In this paper, we propose Patience-based Early Exit, a straightforward y...

0 Wangchunshu Zhou, et al. ∙

research

∙ 04/28/2020

Scheduled DropHead: A Regularization Method for Transformer Models

In this paper, we introduce DropHead, a structured dropout method specif...

0 Wangchunshu Zhou, et al. ∙

research

∙ 02/12/2020

Learning to Compare for Better Training and Evaluation of Open Domain Natural Language Generation Models

Automated evaluation of open domain natural language generation (NLG) mo...

0 Wangchunshu Zhou, et al. ∙

research

∙ 02/07/2020

BERT-of-Theseus: Compressing BERT by Progressive Module Replacing

In this paper, we propose a novel model compression approach to effectiv...

0 Canwen Xu, et al. ∙

research

∙ 01/31/2020

Pseudo-Bidirectional Decoding for Local Sequence Transduction

Local sequence transduction (LST) tasks are sequence transduction tasks ...

0 Wangchunshu Zhou, et al. ∙

research

∙ 01/31/2020

Self-Adversarial Learning with Comparative Discrimination for Text Generation

Conventional Generative Adversarial Networks (GANs) for text generation ...

0 Wangchunshu Zhou, et al. ∙

research

∙ 11/07/2019

Improving Grammatical Error Correction with Machine Translation Pairs

We propose a novel data synthesis method to generate diverse error-corre...

0 Wangchunshu Zhou, et al. ∙

Wangchunshu Zhou

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