b'Can Xu'

research

∙ 09/12/2023

Re-Reading Improves Reasoning in Language Models

Reasoning presents a significant and challenging issue for Large Languag...

0 Xiaohan Xu, et al. ∙

research

∙ 08/18/2023

WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct

Large language models (LLMs), such as GPT-4, have shown remarkable perfo...

0 Haipeng Luo, et al. ∙

research

∙ 07/28/2023

Investigating the Learning Behaviour of In-context Learning: A Comparison with Supervised Learning

Large language models (LLMs) have shown remarkable capacity for in-conte...

0 Xindi Wang, et al. ∙

research

∙ 06/14/2023

WizardCoder: Empowering Code Large Language Models with Evol-Instruct

Code Large Language Models (Code LLMs), such as StarCoder, have demonstr...

0 Ziyang Luo, et al. ∙

research

∙ 05/12/2023

Knowledge Refinement via Interaction Between Search Engines and Large Language Models

Information retrieval (IR) plays a crucial role in locating relevant res...

0 Jiazhan Feng, et al. ∙

research

∙ 05/08/2023

Augmented Large Language Models with Parametric Knowledge Guiding

Large Language Models (LLMs) have significantly advanced natural languag...

0 Ziyang Luo, et al. ∙

research

∙ 04/26/2023

Self-Supervised Multi-Modal Sequential Recommendation

With the increasing development of e-commerce and online services, perso...

0 Kunzhe Song, et al. ∙

research

∙ 04/24/2023

WizardLM: Empowering Large Language Models to Follow Complex Instructions

Training large language models (LLM) with open-domain instruction follow...

0 Can Xu, et al. ∙

research

∙ 02/06/2023

LexLIP: Lexicon-Bottlenecked Language-Image Pre-Training for Large-Scale Image-Text Retrieval

Image-text retrieval (ITR) is a task to retrieve the relevant images/tex...

0 Ziyang Luo, et al. ∙

research

∙ 12/20/2022

Fine-Grained Distillation for Long Document Retrieval

Long document retrieval aims to fetch query-relevant documents from a la...

0 Yucheng Zhou, et al. ∙

research

∙ 12/20/2022

Adam: Dense Retrieval Distillation with Adaptive Dark Examples

To improve the performance of the dual-encoder retriever, one effective ...

0 Chang Liu, et al. ∙

research

∙ 11/10/2022

MMDialog: A Large-scale Multi-turn Dialogue Dataset Towards Multi-modal Open-domain Conversation

Responding with multi-modal content has been recognized as an essential ...

0 Jiazhan Feng, et al. ∙

research

∙ 08/31/2022

LexMAE: Lexicon-Bottlenecked Pretraining for Large-Scale Retrieval

In large-scale retrieval, the lexicon-weighting paradigm, learning weigh...

0 Tao Shen, et al. ∙

research

∙ 08/29/2022

LED: Lexicon-Enlightened Dense Retriever for Large-Scale Retrieval

Retrieval models based on dense representations in semantic space have b...

0 Kai Zhang, et al. ∙

research

∙ 08/06/2022

LFGCF: Light Folksonomy Graph Collaborative Filtering for Tag-Aware Recommendation

Tag-aware recommendation is a task of predicting a personalized list of ...

0 Yin Zhang, et al. ∙

research

∙ 06/21/2022

KnowDA: All-in-One Knowledge Mixture Model for Data Augmentation in Few-Shot NLP

This paper focuses on text data augmentation for few-shot NLP tasks. The...

0 Yufei Wang, et al. ∙

research

∙ 06/16/2022

Towards Robust Ranker for Text Retrieval

A ranker plays an indispensable role in the de facto 'retrieval rera...

0 Yucheng Zhou, et al. ∙

research

∙ 05/23/2022

UnifieR: A Unified Retriever for Large-Scale Retrieval

Large-scale retrieval is to recall relevant documents from a huge collec...

0 Tao Shen, et al. ∙

research

∙ 04/12/2022

Stylized Knowledge-Grounded Dialogue Generation via Disentangled Template Rewriting

Current Knowledge-Grounded Dialogue Generation (KDG) models specialize i...

0 Qingfeng Sun, et al. ∙

research

∙ 03/16/2022

TegTok: Augmenting Text Generation via Task-specific and Open-world Knowledge

Generating natural and informative texts has been a long-standing proble...

0 Chao-Hong Tan, et al. ∙

research

∙ 02/25/2022

PromDA: Prompt-based Data Augmentation for Low-Resource NLU Tasks

This paper focuses on the Data Augmentation for low-resource Natural Lan...

0 Yufei Wang, et al. ∙

research

∙ 01/28/2022

PCL: Peer-Contrastive Learning with Diverse Augmentations for Unsupervised Sentence Embeddings

Learning sentence embeddings in an unsupervised manner is fundamental in...

0 Qiyu Wu, et al. ∙

research

∙ 01/26/2022

Recency Dropout for Recurrent Recommender Systems

Recurrent recommender systems have been successful in capturing the temp...

2 Bo Chang, et al. ∙

research

∙ 11/19/2021

Small Changes Make Big Differences: Improving Multi-turn Response Selection in Dialogue Systems via Fine-Grained Contrastive Learning

Retrieve-based dialogue response selection aims to find a proper respons...

0 Yuntao Li, et al. ∙

research

∙ 10/16/2021

Multimodal Dialogue Response Generation

Responsing with image has been recognized as an important capability for...

20 Qingfeng Sun, et al. ∙

research

∙ 10/14/2021

Finetuning Large-Scale Pre-trained Language Models for Conversational Recommendation with Knowledge Graph

In this paper, we present a pre-trained language model (PLM) based frame...

0 Lingzhi Wang, et al. ∙

research

∙ 09/25/2021

Learning Neural Templates for Recommender Dialogue System

Though recent end-to-end neural models have shown promising progress on ...

0 Zujie Liang, et al. ∙

research

∙ 09/24/2021

Contextual Fine-to-Coarse Distillation for Coarse-grained Response Selection in Open-Domain Conversations

We study the problem of coarse-grained response selection in retrieval-b...

0 Wei Chen, et al. ∙

research

∙ 09/13/2021

Learning to Ground Visual Objects for Visual Dialog

Visual dialog is challenging since it needs to answer a series of cohere...

0 Feilong Chen, et al. ∙

research

∙ 06/03/2021

MPC-BERT: A Pre-Trained Language Model for Multi-Party Conversation Understanding

Recently, various neural models for multi-party conversation (MPC) have ...

0 Jia-Chen Gu, et al. ∙

research

∙ 05/27/2021

Maria: A Visual Experience Powered Conversational Agent

Arguably, the visual perception of conversational agents to the physical...

2 Zujie Liang, et al. ∙

research

∙ 04/16/2021

ProphetNet-X: Large-Scale Pre-training Models for English, Chinese, Multi-lingual, Dialog, and Code Generation

Now, the pre-training technique is ubiquitous in natural language proces...

0 Weizhen Qi, et al. ∙

research

∙ 01/28/2021

Learning Matching Representations for Individualized Organ Transplantation Allocation

Organ transplantation is often the last resort for treating end-stage il...

4 Can Xu, et al. ∙

research

∙ 11/19/2020

Are Pre-trained Language Models Knowledgeable to Ground Open Domain Dialogues?

We study knowledge-grounded dialogue generation with pre-trained languag...

0 Yufan Zhao, et al. ∙

research

∙ 10/06/2020

StyleDGPT: Stylized Response Generation with Pre-trained Language Models

Generating responses following a desired style has great potentials to e...

0 Wei Wu, et al. ∙

research

∙ 08/29/2020

Zero-Resource Knowledge-Grounded Dialogue Generation

While neural conversation models have shown great potentials towards gen...

0 Linxiao Li, et al. ∙

research

∙ 04/04/2020

Open Domain Dialogue Generation with Latent Images

We consider grounding open domain dialogues with images. Existing work a...

7 Wei Wu, et al. ∙

research

∙ 04/04/2020

Learning a Simple and Effective Model for Multi-turn Response Generation with Auxiliary Tasks

We study multi-turn response generation for open-domain dialogues. The e...

0 Yufan Zhao, et al. ∙

research

∙ 02/24/2020

Low-Resource Knowledge-Grounded Dialogue Generation

Responding with knowledge has been recognized as an important capability...

0 Xueliang Zhao, et al. ∙

research

∙ 12/25/2019

THUEE system description for NIST 2019 SRE CTS Challenge

This paper describes the systems submitted by the department of electron...

0 YI LIU, et al. ∙

research

∙ 09/26/2019

Read, Attend and Comment: A Deep Architecture for Automatic News Comment Generation

Automatic news comment generation is beneficial for real applications bu...

0 Can Xu, et al. ∙

research

∙ 09/26/2019

Low-Resource Response Generation with Template Prior

We study open domain response generation with limited message-response p...

0 Wei Wu, et al. ∙

research

∙ 06/14/2019

Neural Response Generation with Meta-Words

We present open domain response generation with meta-words. A meta-word ...

0 Can Xu, et al. ∙

research

∙ 06/11/2019

A Document-grounded Matching Network for Response Selection in Retrieval-based Chatbots

We present a document-grounded matching network (DGMN) for response sele...

0 Xueliang Zhao, et al. ∙

research

∙ 02/22/2019

Towards Neural Mixture Recommender for Long Range Dependent User Sequences

Understanding temporal dynamics has proved to be highly valuable for acc...

20 Jiaxi Tang, et al. ∙

research

∙ 08/25/2018

Multiobjective Optimization Training of PLDA for Speaker Verification

Most current state-of-the-art text-independent speaker verification syst...

0 Liang He, et al. ∙

research

∙ 08/23/2018

Playing 20 Question Game with Policy-Based Reinforcement Learning

The 20 Questions (Q20) game is a well known game which encourages deduct...

0 Huang Hu, et al. ∙

research

∙ 08/22/2018

Improving Matching Models with Contextualized Word Representations for Multi-turn Response Selection in Retrieval-based Chatbots

We consider matching with pre-trained contextualized word vectors for mu...

0 Chongyang Tao, et al. ∙

research

∙ 07/19/2018

Towards Explainable and Controllable Open Domain Dialogue Generation with Dialogue Acts

We study open domain dialogue generation with dialogue acts designed to ...

0 Can Xu, et al. ∙

research

∙ 11/30/2017

Neural Response Generation with Dynamic Vocabularies

We study response generation for open domain conversation in chatbots. E...

0 Yu Wu, et al. ∙

Can Xu

Featured Co-authors