Search Results for author: Wayne Xin Zhao

Found 118 papers, 79 papers with code

BAMBOO: A Comprehensive Benchmark for Evaluating Long Text Modeling Capacities of Large Language Models

no code implementations23 Sep 2023 Zican Dong, Tianyi Tang, Junyi Li, Wayne Xin Zhao, Ji-Rong Wen

Recently, multiple studies have committed to extending the context length and enhancing the long text modeling capabilities of LLMs.

Code Completion Language Modelling +1

Unified Data Management and Comprehensive Performance Evaluation for Urban Spatial-Temporal Prediction [Experiment, Analysis & Benchmark]

1 code implementation24 Aug 2023 Jiawei Jiang, Chengkai Han, Wayne Xin Zhao, Jingyuan Wang

The field of urban spatial-temporal prediction is advancing rapidly with the development of deep learning techniques and the availability of large-scale datasets.


A Survey on Large Language Model based Autonomous Agents

2 code implementations22 Aug 2023 Lei Wang, Chen Ma, Xueyang Feng, Zeyu Zhang, Hao Yang, Jingsen Zhang, ZhiYuan Chen, Jiakai Tang, Xu Chen, Yankai Lin, Wayne Xin Zhao, Zhewei Wei, Ji-Rong Wen

In this paper, we present a comprehensive survey of these studies, delivering a systematic review of the field of LLM-based autonomous agents from a holistic perspective.

Language Modelling Large Language Model

Towards Effective Ancient Chinese Translation: Dataset, Model, and Evaluation

no code implementations1 Aug 2023 Geyang Guo, Jiarong Yang, Fengyuan LU, Jiaxin Qin, Tianyi Tang, Wayne Xin Zhao

From an evaluation perspective, we build a benchmark to judge ancient Chinese translation quality in different scenarios and evaluate the ancient Chinese translation capacities of various existing models.

Language Modelling Translation

Alleviating the Long-Tail Problem in Conversational Recommender Systems

no code implementations21 Jul 2023 Zhipeng Zhao, Kun Zhou, Xiaolei Wang, Wayne Xin Zhao, Fan Pan, Zhao Cao, Ji-Rong Wen

Conversational recommender systems (CRS) aim to provide the recommendation service via natural language conversations.

Recommendation Systems Retrieval

Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation

1 code implementation20 Jul 2023 Ruiyang Ren, Yuhao Wang, Yingqi Qu, Wayne Xin Zhao, Jing Liu, Hao Tian, Hua Wu, Ji-Rong Wen, Haifeng Wang

In this study, we present an initial analysis of the factual knowledge boundaries of LLMs and how retrieval augmentation affects LLMs on open-domain QA.

Open-Domain Question Answering Retrieval

Do Emergent Abilities Exist in Quantized Large Language Models: An Empirical Study

1 code implementation16 Jul 2023 Peiyu Liu, Zikang Liu, Ze-Feng Gao, Dawei Gao, Wayne Xin Zhao, Yaliang Li, Bolin Ding, Ji-Rong Wen

Different from previous studies focused on overall performance, this work aims to investigate the impact of quantization on \emph{emergent abilities}, which are important characteristics that distinguish LLMs from small language models.

Instruction Following Quantization

Reciprocal Sequential Recommendation

1 code implementation26 Jun 2023 Bowen Zheng, Yupeng Hou, Wayne Xin Zhao, Yang song, HengShu Zhu

Existing RRS models mainly capture static user preferences, which have neglected the evolving user tastes and the dynamic matching relation between the two parties.

Sequential Recommendation

Improving Conversational Recommendation Systems via Counterfactual Data Simulation

1 code implementation5 Jun 2023 Xiaolei Wang, Kun Zhou, Xinyu Tang, Wayne Xin Zhao, Fan Pan, Zhao Cao, Ji-Rong Wen

To develop our approach, we characterize user preference and organize the conversation flow by the entities involved in the dialogue, and design a multi-stage recommendation dialogue simulator based on a conversation flow language model.

Data Augmentation Language Modelling +1

When Large Language Model based Agent Meets User Behavior Analysis: A Novel User Simulation Paradigm

1 code implementation5 Jun 2023 Lei Wang, Jingsen Zhang, Hao Yang, ZhiYuan Chen, Jiakai Tang, Zeyu Zhang, Xu Chen, Yankai Lin, Ruihua Song, Wayne Xin Zhao, Jun Xu, Zhicheng Dou, Jun Wang, Ji-Rong Wen

We argue that these models present significant opportunities for reliable user simulation, and have the potential to revolutionize traditional study paradigms in user behavior analysis.

Language Modelling Large Language Model +2

Evaluating and Improving Tool-Augmented Computation-Intensive Math Reasoning

1 code implementation4 Jun 2023 Beichen Zhang, Kun Zhou, Xilin Wei, Wayne Xin Zhao, Jing Sha, Shijin Wang, Ji-Rong Wen

Based on this finding, we propose a new approach that can deliberate the reasoning steps with tool interfaces, namely \textbf{DELI}.

Zero-shot Visual Question Answering with Language Model Feedback

1 code implementation26 May 2023 Yifan Du, Junyi Li, Tianyi Tang, Wayne Xin Zhao, Ji-Rong Wen

In this paper, we propose a novel language model guided captioning approach, LAMOC, for knowledge-based visual question answering (VQA).

Language Modelling Question Answering +1

Not All Metrics Are Guilty: Improving NLG Evaluation with LLM Paraphrasing

1 code implementation24 May 2023 Tianyi Tang, Hongyuan Lu, Yuchen Eleanor Jiang, Haoyang Huang, Dongdong Zhang, Wayne Xin Zhao, Furu Wei

Most research about natural language generation (NLG) relies on evaluation benchmarks with limited references for a sample, which may result in poor correlations with human judgements.

Machine Translation Text Generation +1

ChatCoT: Tool-Augmented Chain-of-Thought Reasoning on Chat-based Large Language Models

1 code implementation23 May 2023 Zhipeng Chen, Kun Zhou, Beichen Zhang, Zheng Gong, Wayne Xin Zhao, Ji-Rong Wen

To improve the reasoning abilities, we propose \textbf{ChatCoT}, a tool-augmented chain-of-thought reasoning framework for chat-based LLMs.

Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Models

1 code implementation22 May 2023 Xiaolei Wang, Xinyu Tang, Wayne Xin Zhao, Jingyuan Wang, Ji-Rong Wen

The recent success of large language models (LLMs) has shown great potential to develop more powerful conversational recommender systems (CRSs), which rely on natural language conversations to satisfy user needs.

Explanation Generation Recommendation Systems

HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Models

2 code implementations19 May 2023 Junyi Li, Xiaoxue Cheng, Wayne Xin Zhao, Jian-Yun Nie, Ji-Rong Wen

To understand what types of content and to which extent LLMs are apt to hallucinate, we introduce the Hallucination Evaluation for Large Language Models (HaluEval) benchmark, a large collection of generated and human-annotated hallucinated samples for evaluating the performance of LLMs in recognizing hallucination.

The Web Can Be Your Oyster for Improving Large Language Models

1 code implementation18 May 2023 Junyi Li, Tianyi Tang, Wayne Xin Zhao, Jingyuan Wang, Jian-Yun Nie, Ji-Rong Wen

In order to further improve the capacity of LLMs for knowledge-intensive tasks, we consider augmenting LLMs with the large-scale web using search engine.


TOME: A Two-stage Approach for Model-based Retrieval

no code implementations18 May 2023 Ruiyang Ren, Wayne Xin Zhao, Jing Liu, Hua Wu, Ji-Rong Wen, Haifeng Wang

Recently, model-based retrieval has emerged as a new paradigm in text retrieval that discards the index in the traditional retrieval model and instead memorizes the candidate corpora using model parameters.

Natural Questions Retrieval +1

Evaluating Object Hallucination in Large Vision-Language Models

2 code implementations17 May 2023 YiFan Li, Yifan Du, Kun Zhou, Jinpeng Wang, Wayne Xin Zhao, Ji-Rong Wen

Despite the promising progress on LVLMs, we find that LVLMs suffer from the hallucination problem, i. e. they tend to generate objects that are inconsistent with the target images in the descriptions.

StructGPT: A General Framework for Large Language Model to Reason over Structured Data

1 code implementation16 May 2023 Jinhao Jiang, Kun Zhou, Zican Dong, Keming Ye, Wayne Xin Zhao, Ji-Rong Wen

Specially, we propose an \emph{invoking-linearization-generation} procedure to support LLMs in reasoning on the structured data with the help of the external interfaces.

Language Modelling Large Language Model +1

Large Language Models are Zero-Shot Rankers for Recommender Systems

1 code implementation15 May 2023 Yupeng Hou, Junjie Zhang, Zihan Lin, Hongyu Lu, Ruobing Xie, Julian McAuley, Wayne Xin Zhao

Along this line of research, this work aims to investigate the capacity of LLMs that act as the ranking model for recommender systems.

Recommendation Systems

Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach

no code implementations11 May 2023 Junjie Zhang, Ruobing Xie, Yupeng Hou, Wayne Xin Zhao, Leyu Lin, Ji-Rong Wen

Inspired by the recent progress on large language models (LLMs), we take a different approach to developing the recommendation models, considering recommendation as instruction following by LLMs.

Instruction Following Language Modelling +2

Diffusion-NAT: Self-Prompting Discrete Diffusion for Non-Autoregressive Text Generation

no code implementations6 May 2023 Kun Zhou, YiFan Li, Wayne Xin Zhao, Ji-Rong Wen

To solve it, we propose Diffusion-NAT, which introduces discrete diffusion models~(DDM) into NAR text-to-text generation and integrates BART to improve the performance.

Denoising Text Generation

Multi-grained Hypergraph Interest Modeling for Conversational Recommendation

1 code implementation4 May 2023 Chenzhan Shang, Yupeng Hou, Wayne Xin Zhao, Yaliang Li, Jing Zhang

In our approach, we first employ the hypergraph structure to model users' historical dialogue sessions and form a session-based hypergraph, which captures coarse-grained, session-level relations.

Recommendation Systems

LibCity: A Unified Library Towards Efficient and Comprehensive Urban Spatial-Temporal Prediction

2 code implementations27 Apr 2023 Jiawei Jiang, Chengkai Han, Wenjun Jiang, Wayne Xin Zhao, Jingyuan Wang

As deep learning technology advances and more urban spatial-temporal data accumulates, an increasing number of deep learning models are being proposed to solve urban spatial-temporal prediction problems.

GlyphDiffusion: Text Generation as Image Generation

no code implementations25 Apr 2023 Junyi Li, Wayne Xin Zhao, Jian-Yun Nie, Ji-Rong Wen

In this way, conditional text generation can be cast as a glyph image generation task, and it is then natural to apply continuous diffusion models to discrete texts.

Conditional Text Generation Glyph Image Generation +2

EulerNet: Adaptive Feature Interaction Learning via Euler's Formula for CTR Prediction

1 code implementation21 Apr 2023 Zhen Tian, Ting Bai, Wayne Xin Zhao, Ji-Rong Wen, Zhao Cao

EulerNet converts the exponential powers of feature interactions into simple linear combinations of the modulus and phase of the complex features, making it possible to adaptively learn the high-order feature interactions in an efficient way.

Click-Through Rate Prediction

A Survey of Large Language Models

1 code implementation31 Mar 2023 Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, Yifan Du, Chen Yang, Yushuo Chen, Zhipeng Chen, Jinhao Jiang, Ruiyang Ren, YiFan Li, Xinyu Tang, Zikang Liu, Peiyu Liu, Jian-Yun Nie, Ji-Rong Wen

To discriminate the difference in parameter scale, the research community has coined the term large language models (LLM) for the PLMs of significant size.

Language Modelling

Scaling Pre-trained Language Models to Deeper via Parameter-efficient Architecture

no code implementations27 Mar 2023 Peiyu Liu, Ze-Feng Gao, Yushuo Chen, Wayne Xin Zhao, Ji-Rong Wen

Based on such a decomposition, our architecture shares the central tensor across all layers for reducing the model size and meanwhile keeps layer-specific auxiliary tensors (also using adapters) for enhancing the adaptation flexibility.

Diffusion Models for Non-autoregressive Text Generation: A Survey

1 code implementation12 Mar 2023 YiFan Li, Kun Zhou, Wayne Xin Zhao, Ji-Rong Wen

In this survey, we review the recent progress in diffusion models for NAR text generation.

Text Generation

A Survey on Long Text Modeling with Transformers

no code implementations28 Feb 2023 Zican Dong, Tianyi Tang, Lunyi Li, Wayne Xin Zhao

In this paper, we provide an overview of the recent advances on long texts modeling based on Transformer models.

Hybrid Contrastive Constraints for Multi-Scenario Ad Ranking

no code implementations6 Feb 2023 Shanlei Mu, Penghui Wei, Wayne Xin Zhao, Shaoguo Liu, Liang Wang, Bo Zheng

In this paper, we propose a Hybrid Contrastive Constrained approach (HC^2) for multi-scenario ad ranking.

Contrastive Learning

PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Prediction

1 code implementation19 Jan 2023 Jiawei Jiang, Chengkai Han, Wayne Xin Zhao, Jingyuan Wang

However, GNN-based models have three major limitations for traffic prediction: i) Most methods model spatial dependencies in a static manner, which limits the ability to learn dynamic urban traffic patterns; ii) Most methods only consider short-range spatial information and are unable to capture long-range spatial dependencies; iii) These methods ignore the fact that the propagation of traffic conditions between locations has a time delay in traffic systems.

Time Series Prediction Traffic Prediction

Continuous Trajectory Generation Based on Two-Stage GAN

no code implementations16 Jan 2023 Wenjun Jiang, Wayne Xin Zhao, Jingyuan Wang, Jiawei Jiang

Simulating the human mobility and generating large-scale trajectories are of great use in many real-world applications, such as urban planning, epidemic spreading analysis, and geographic privacy protect.

Vocal Bursts Valence Prediction

TikTalk: A Video-Based Dialogue Dataset for Multi-Modal Chitchat in Real World

1 code implementation14 Jan 2023 Hongpeng Lin, Ludan Ruan, Wenke Xia, Peiyu Liu, Jingyuan Wen, Yixin Xu, Di Hu, Ruihua Song, Wayne Xin Zhao, Qin Jin, Zhiwu Lu

Experimental results indicate that the models incorporating large language models (LLM) can generate more diverse responses, while the model utilizing knowledge graphs to introduce external knowledge performs the best overall.

Knowledge Graphs

TextBox 2.0: A Text Generation Library with Pre-trained Language Models

1 code implementation26 Dec 2022 Tianyi Tang, Junyi Li, Zhipeng Chen, Yiwen Hu, Zhuohao Yu, Wenxun Dai, Zican Dong, Xiaoxue Cheng, Yuhao Wang, Wayne Xin Zhao, Jian-Yun Nie, Ji-Rong Wen

To facilitate research on text generation, this paper presents a comprehensive and unified library, TextBox 2. 0, focusing on the use of pre-trained language models (PLMs).

Abstractive Text Summarization Data-to-Text Generation +7

MASTER: Multi-task Pre-trained Bottlenecked Masked Autoencoders are Better Dense Retrievers

1 code implementation15 Dec 2022 Kun Zhou, Xiao Liu, Yeyun Gong, Wayne Xin Zhao, Daxin Jiang, Nan Duan, Ji-Rong Wen

Pre-trained Transformers (\eg BERT) have been commonly used in existing dense retrieval methods for parameter initialization, and recent studies are exploring more effective pre-training tasks for further improving the quality of dense vectors.

Passage Retrieval Retrieval

Visually-augmented pretrained language models for NLP tasks without images

1 code implementation15 Dec 2022 Hangyu Guo, Kun Zhou, Wayne Xin Zhao, Qinyu Zhang, Ji-Rong Wen

Although pre-trained language models~(PLMs) have shown impressive performance by text-only self-supervised training, they are found lack of visual semantics or commonsense.


UniKGQA: Unified Retrieval and Reasoning for Solving Multi-hop Question Answering Over Knowledge Graph

1 code implementation2 Dec 2022 Jinhao Jiang, Kun Zhou, Wayne Xin Zhao, Ji-Rong Wen

Multi-hop Question Answering over Knowledge Graph~(KGQA) aims to find the answer entities that are multiple hops away from the topic entities mentioned in a natural language question on a large-scale Knowledge Graph (KG).

Language Modelling Multi-hop Question Answering +2

Recent Advances in RecBole: Extensions with more Practical Considerations

1 code implementation28 Nov 2022 Lanling Xu, Zhen Tian, Gaowei Zhang, Lei Wang, Junjie Zhang, Bowen Zheng, YiFan Li, Yupeng Hou, Xingyu Pan, Yushuo Chen, Wayne Xin Zhao, Xu Chen, Ji-Rong Wen

In order to show the recent update in RecBole, we write this technical report to introduce our latest improvements on RecBole.

Dense Text Retrieval based on Pretrained Language Models: A Survey

2 code implementations27 Nov 2022 Wayne Xin Zhao, Jing Liu, Ruiyang Ren, Ji-Rong Wen

With powerful PLMs, we can effectively learn the representations of queries and texts in the latent representation space, and further construct the semantic matching function between the dense vectors for relevance modeling.

Retrieval Text Retrieval

Directed Acyclic Graph Factorization Machines for CTR Prediction via Knowledge Distillation

1 code implementation21 Nov 2022 Zhen Tian, Ting Bai, Zibin Zhang, Zhiyuan Xu, Kangyi Lin, Ji-Rong Wen, Wayne Xin Zhao

Some recent knowledge distillation based methods transfer knowledge from complex teacher models to shallow student models for accelerating the online model inference.

Click-Through Rate Prediction Knowledge Distillation +1

Learning Vector-Quantized Item Representation for Transferable Sequential Recommenders

1 code implementation22 Oct 2022 Yupeng Hou, Zhankui He, Julian McAuley, Wayne Xin Zhao

Based on this representation scheme, we further propose an enhanced contrastive pre-training approach, using semi-synthetic and mixed-domain code representations as hard negatives.

Language Modelling Recommendation Systems +1

SimANS: Simple Ambiguous Negatives Sampling for Dense Text Retrieval

1 code implementation21 Oct 2022 Kun Zhou, Yeyun Gong, Xiao Liu, Wayne Xin Zhao, Yelong Shen, Anlei Dong, Jingwen Lu, Rangan Majumder, Ji-Rong Wen, Nan Duan, Weizhu Chen

Thus, we propose a simple ambiguous negatives sampling method, SimANS, which incorporates a new sampling probability distribution to sample more ambiguous negatives.

Retrieval Text Retrieval

Privacy-Preserved Neural Graph Similarity Learning

1 code implementation21 Oct 2022 Yupeng Hou, Wayne Xin Zhao, Yaliang Li, Ji-Rong Wen

To develop effective and efficient graph similarity learning (GSL) models, a series of data-driven neural algorithms have been proposed in recent years.

Graph Matching Graph Similarity +1

Modeling Adaptive Fine-grained Task Relatedness for Joint CTR-CVR Estimation

no code implementations29 Aug 2022 Zihan Lin, Xuanhua Yang, Xiaoyu Peng, Wayne Xin Zhao, Shaoguo Liu, Liang Wang, Bo Zheng

For this purpose, we build a relatedness prediction network, so that it can predict the contrast strength for inter-task representations of an instance.

Contrastive Learning Multi-Task Learning +2

Modeling Two-Way Selection Preference for Person-Job Fit

1 code implementation18 Aug 2022 Chen Yang, Yupeng Hou, Yang song, Tao Zhang, Ji-Rong Wen, Wayne Xin Zhao

To model the two-way selection preference from the dual-perspective of job seekers and employers, we incorporate two different nodes for each candidate (or job) and characterize both successful matching and failed matching via a unified dual-perspective interaction graph.

Contrastive Learning Graph Representation Learning +1

MVP: Multi-task Supervised Pre-training for Natural Language Generation

2 code implementations24 Jun 2022 Tianyi Tang, Junyi Li, Wayne Xin Zhao, Ji-Rong Wen

Motivated by the success of supervised pre-training, we propose Multi-task superVised Pre-training (MVP) for natural language generation.

Text Generation

Towards Unified Conversational Recommender Systems via Knowledge-Enhanced Prompt Learning

1 code implementation19 Jun 2022 Xiaolei Wang, Kun Zhou, Ji-Rong Wen, Wayne Xin Zhao

Our approach unifies the recommendation and conversation subtasks into the prompt learning paradigm, and utilizes knowledge-enhanced prompts based on a fixed pre-trained language model (PLM) to fulfill both subtasks in a unified approach.

Language Modelling Recommendation Systems +1

RecBole 2.0: Towards a More Up-to-Date Recommendation Library

2 code implementations15 Jun 2022 Wayne Xin Zhao, Yupeng Hou, Xingyu Pan, Chen Yang, Zeyu Zhang, Zihan Lin, Jingsen Zhang, Shuqing Bian, Jiakai Tang, Wenqi Sun, Yushuo Chen, Lanling Xu, Gaowei Zhang, Zhen Tian, Changxin Tian, Shanlei Mu, Xinyan Fan, Xu Chen, Ji-Rong Wen

In order to support the study of recent advances in recommender systems, this paper presents an extended recommendation library consisting of eight packages for up-to-date topics and architectures.

Benchmarking Data Augmentation +3

Towards Universal Sequence Representation Learning for Recommender Systems

1 code implementation13 Jun 2022 Yupeng Hou, Shanlei Mu, Wayne Xin Zhao, Yaliang Li, Bolin Ding, Ji-Rong Wen

In order to develop effective sequential recommenders, a series of sequence representation learning (SRL) methods are proposed to model historical user behaviors.

Recommendation Systems Representation Learning

JiuZhang: A Chinese Pre-trained Language Model for Mathematical Problem Understanding

1 code implementation13 Jun 2022 Wayne Xin Zhao, Kun Zhou, Zheng Gong, Beichen Zhang, Yuanhang Zhou, Jing Sha, Zhigang Chen, Shijin Wang, Cong Liu, Ji-Rong Wen

Considering the complex nature of mathematical texts, we design a novel curriculum pre-training approach for improving the learning of mathematical PLMs, consisting of both basic and advanced courses.

Language Modelling

Feature-aware Diversified Re-ranking with Disentangled Representations for Relevant Recommendation

no code implementations10 Jun 2022 Zihan Lin, Hui Wang, Jingshu Mao, Wayne Xin Zhao, Cheng Wang, Peng Jiang, Ji-Rong Wen

Relevant recommendation is a special recommendation scenario which provides relevant items when users express interests on one target item (e. g., click, like and purchase).

Disentanglement Re-Ranking

ID-Agnostic User Behavior Pre-training for Sequential Recommendation

no code implementations6 Jun 2022 Shanlei Mu, Yupeng Hou, Wayne Xin Zhao, Yaliang Li, Bolin Ding

Instead of explicitly learning representations for item IDs, IDA-SR directly learns item representations from rich text information.

Language Modelling Sequential Recommendation

Negative Sampling for Contrastive Representation Learning: A Review

no code implementations1 Jun 2022 Lanling Xu, Jianxun Lian, Wayne Xin Zhao, Ming Gong, Linjun Shou, Daxin Jiang, Xing Xie, Ji-Rong Wen

The learn-to-compare paradigm of contrastive representation learning (CRL), which compares positive samples with negative ones for representation learning, has achieved great success in a wide range of domains, including natural language processing, computer vision, information retrieval and graph learning.

Graph Learning Information Retrieval +2

Learning to Transfer Prompts for Text Generation

1 code implementation NAACL 2022 Junyi Li, Tianyi Tang, Jian-Yun Nie, Ji-Rong Wen, Wayne Xin Zhao

First, PTG learns a set of source prompts for various source generation tasks and then transfers these prompts as target prompts to perform target generation tasks.

Text Generation

Debiased Contrastive Learning of Unsupervised Sentence Representations

1 code implementation ACL 2022 Kun Zhou, Beichen Zhang, Wayne Xin Zhao, Ji-Rong Wen

In DCLR, we design an instance weighting method to punish false negatives and generate noise-based negatives to guarantee the uniformity of the representation space.

Contrastive Learning Semantic Textual Similarity

A Thorough Examination on Zero-shot Dense Retrieval

no code implementations27 Apr 2022 Ruiyang Ren, Yingqi Qu, Jing Liu, Wayne Xin Zhao, Qifei Wu, Yuchen Ding, Hua Wu, Haifeng Wang, Ji-Rong Wen

Recent years have witnessed the significant advance in dense retrieval (DR) based on powerful pre-trained language models (PLM).


CORE: Simple and Effective Session-based Recommendation within Consistent Representation Space

1 code implementation23 Apr 2022 Yupeng Hou, Binbin Hu, Zhiqiang Zhang, Wayne Xin Zhao

Session-based Recommendation (SBR) refers to the task of predicting the next item based on short-term user behaviors within an anonymous session.

Session-Based Recommendations

Leveraging Search History for Improving Person-Job Fit

no code implementations27 Mar 2022 Yupeng Hou, Xingyu Pan, Wayne Xin Zhao, Shuqing Bian, Yang song, Tao Zhang, Ji-Rong Wen

As the core technique of online recruitment platforms, person-job fit can improve hiring efficiency by accurately matching job positions with qualified candidates.

Text Matching

WuDaoMM: A large-scale Multi-Modal Dataset for Pre-training models

no code implementations22 Mar 2022 Sha Yuan, Shuai Zhao, Jiahong Leng, Zhao Xue, Hanyu Zhao, Peiyu Liu, Zheng Gong, Wayne Xin Zhao, Junyi Li, Jie Tang

The results show that WuDaoMM can be applied as an efficient dataset for VLPMs, especially for the model in text-to-image generation task.

Image Captioning Question Answering +1

Neural Graph Matching for Pre-training Graph Neural Networks

1 code implementation3 Mar 2022 Yupeng Hou, Binbin Hu, Wayne Xin Zhao, Zhiqiang Zhang, Jun Zhou, Ji-Rong Wen

In this way, we can learn adaptive representations for a given graph when paired with different graphs, and both node- and graph-level characteristics are naturally considered in a single pre-training task.

Graph Matching

Parameter-Efficient Mixture-of-Experts Architecture for Pre-trained Language Models

2 code implementations COLING 2022 Ze-Feng Gao, Peiyu Liu, Wayne Xin Zhao, Zhong-Yi Lu, Ji-Rong Wen

Recently, Mixture-of-Experts (short as MoE) architecture has achieved remarkable success in increasing the model capacity of large-scale language models.

Language Modelling Multi-Task Learning +2

Filter-enhanced MLP is All You Need for Sequential Recommendation

1 code implementation28 Feb 2022 Kun Zhou, Hui Yu, Wayne Xin Zhao, Ji-Rong Wen

Recently, deep neural networks such as RNN, CNN and Transformer have been applied in the task of sequential recommendation, which aims to capture the dynamic preference characteristics from logged user behavior data for accurate recommendation.

Sequential Recommendation

A Survey of Vision-Language Pre-Trained Models

no code implementations18 Feb 2022 Yifan Du, Zikang Liu, Junyi Li, Wayne Xin Zhao

In this paper, we review the recent progress in Vision-Language Pre-Trained Models (VL-PTMs).

Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning

1 code implementation13 Feb 2022 Zihan Lin, Changxin Tian, Yupeng Hou, Wayne Xin Zhao

For the structural neighbors on the interaction graph, we develop a novel structure-contrastive objective that regards users (or items) and their structural neighbors as positive contrastive pairs.

Collaborative Filtering Contrastive Learning

Context-Tuning: Learning Contextualized Prompts for Natural Language Generation

1 code implementation COLING 2022 Tianyi Tang, Junyi Li, Wayne Xin Zhao, Ji-Rong Wen

Secondly, we use continuous inverse prompting to improve the process of natural language generation by modeling an inverse generation process from output to input, making the generated text more relevant to the inputs.

Text Generation

Pretrained Language Models for Text Generation: A Survey

no code implementations14 Jan 2022 Junyi Li, Tianyi Tang, Wayne Xin Zhao, Jian-Yun Nie, Ji-Rong Wen

We begin with introducing three key aspects of applying PLMs to text generation: 1) how to encode the input into representations preserving input semantics which can be fused into PLMs; 2) how to design an effective PLM to serve as the generation model; and 3) how to effectively optimize PLMs given the reference text and to ensure that the generated texts satisfy special text properties.

Text Generation

C2-CRS: Coarse-to-Fine Contrastive Learning for Conversational Recommender System

1 code implementation4 Jan 2022 Yuanhang Zhou, Kun Zhou, Wayne Xin Zhao, Cheng Wang, Peng Jiang, He Hu

To implement this framework, we design both coarse-grained and fine-grained procedures for modeling user preference, where the former focuses on more general, coarse-grained semantic fusion and the latter focuses on more specific, fine-grained semantic fusion.

Contrastive Learning Recommendation Systems +2

LibCity: An Open Library for Traffic Prediction

1 code implementation International Conference on Advances in Geographic Information Systems 2021 Jingyuan Wang, Jiawei Jiang, Wenjun Jiang, Chao Li, Wayne Xin Zhao

This paper presents LibCity, a unified, comprehensive, and extensible library for traffic prediction, which provides researchers with a credible experimental tool and a convenient development framework.

Multivariate Time Series Forecasting Spatio-Temporal Forecasting +2

Curriculum Pre-Training Heterogeneous Subgraph Transformer for Top-$N$ Recommendation

no code implementations12 Jun 2021 Hui Wang, Kun Zhou, Wayne Xin Zhao, Jingyuan Wang, Ji-Rong Wen

Due to the flexibility in modelling data heterogeneity, heterogeneous information network (HIN) has been adopted to characterize complex and heterogeneous auxiliary data in top-$N$ recommender systems, called \emph{HIN-based recommendation}.

Recommendation Systems

Enabling Lightweight Fine-tuning for Pre-trained Language Model Compression based on Matrix Product Operators

1 code implementation ACL 2021 Peiyu Liu, Ze-Feng Gao, Wayne Xin Zhao, Z. Y. Xie, Zhong-Yi Lu, Ji-Rong Wen

This paper presents a novel pre-trained language models (PLM) compression approach based on the matrix product operator (short as MPO) from quantum many-body physics.

Language Modelling Model Compression

Pretrained Language Models for Text Generation: A Survey

no code implementations21 May 2021 Junyi Li, Tianyi Tang, Wayne Xin Zhao, Ji-Rong Wen

In this paper, we present an overview of the major advances achieved in the topic of PLMs for text generation.

Text Generation

Knowledge-based Review Generation by Coherence Enhanced Text Planning

no code implementations9 May 2021 Junyi Li, Wayne Xin Zhao, Zhicheng Wei, Nicholas Jing Yuan, Ji-Rong Wen

For global coherence, we design a hierarchical self-attentive architecture with both subgraph- and node-level attention to enhance the correlations between subgraphs.

Informativeness Knowledge Graphs +2

Improving Multi-hop Knowledge Base Question Answering by Learning Intermediate Supervision Signals

1 code implementation11 Jan 2021 Gaole He, Yunshi Lan, Jing Jiang, Wayne Xin Zhao, Ji-Rong Wen

In our approach, the student network aims to find the correct answer to the query, while the teacher network tries to learn intermediate supervision signals for improving the reasoning capacity of the student network.

Knowledge Base Question Answering Semantic Parsing

TextBox: A Unified, Modularized, and Extensible Framework for Text Generation

1 code implementation ACL 2021 Junyi Li, Tianyi Tang, Gaole He, Jinhao Jiang, Xiaoxuan Hu, Puzhao Xie, Zhipeng Chen, Zhuohao Yu, Wayne Xin Zhao, Ji-Rong Wen

In this paper, we release an open-source library, called TextBox, to provide a unified, modularized, and extensible text generation framework.

Text Generation

RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms

1 code implementation3 Nov 2020 Wayne Xin Zhao, Shanlei Mu, Yupeng Hou, Zihan Lin, Yushuo Chen, Xingyu Pan, Kaiyuan Li, Yujie Lu, Hui Wang, Changxin Tian, Yingqian Min, Zhichao Feng, Xinyan Fan, Xu Chen, Pengfei Wang, Wendi Ji, Yaliang Li, Xiaoling Wang, Ji-Rong Wen

In this library, we implement 73 recommendation models on 28 benchmark datasets, covering the categories of general recommendation, sequential recommendation, context-aware recommendation and knowledge-based recommendation.

Collaborative Filtering Sequential Recommendation

Learning to Match Jobs with Resumes from Sparse Interaction Data using Multi-View Co-Teaching Network

no code implementations25 Sep 2020 Shuqing Bian, Xu Chen, Wayne Xin Zhao, Kun Zhou, Yupeng Hou, Yang song, Tao Zhang, Ji-Rong Wen

Compared with pure text-based matching models, the proposed approach is able to learn better data representations from limited or even sparse interaction data, which is more resistible to noise in training data.

Text Matching

Leveraging Historical Interaction Data for Improving Conversational Recommender System

no code implementations19 Aug 2020 Kun Zhou, Wayne Xin Zhao, Hui Wang, Sirui Wang, Fuzheng Zhang, Zhongyuan Wang, Ji-Rong Wen

Most of the existing CRS methods focus on learning effective preference representations for users from conversation data alone.

Recommendation Systems

S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization

2 code implementations18 Aug 2020 Kun Zhou, Hui Wang, Wayne Xin Zhao, Yutao Zhu, Sirui Wang, Fuzheng Zhang, Zhongyuan Wang, Ji-Rong Wen

To tackle this problem, we propose the model S^3-Rec, which stands for Self-Supervised learning for Sequential Recommendation, based on the self-attentive neural architecture.

Self-Supervised Learning Sequential Recommendation

Improving Multi-Turn Response Selection Models with Complementary Last-Utterance Selection by Instance Weighting

no code implementations18 Feb 2020 Kun Zhou, Wayne Xin Zhao, Yutao Zhu, Ji-Rong Wen, Jingsong Yu

Open-domain retrieval-based dialogue systems require a considerable amount of training data to learn their parameters.


Domain Adaptation for Person-Job Fit with Transferable Deep Global Match Network

no code implementations IJCNLP 2019 Shuqing Bian, Wayne Xin Zhao, Yang song, Tao Zhang, Ji-Rong Wen

Furthermore, we extend the match network and implement domain adaptation in three levels, sentence-level representation, sentence-level match, and global match.

Domain Adaptation

Empowering A* Search Algorithms with Neural Networks for Personalized Route Recommendation

no code implementations19 Jul 2019 Jingyuan Wang, Ning Wu, Wayne Xin Zhao, Fanzhang Peng, Xin Lin

To address these issues, we propose using neural networks to automatically learn the cost functions of a classic heuristic algorithm, namely A* algorithm, for the PRR task.

Graph Attention

Generating Long and Informative Reviews with Aspect-Aware Coarse-to-Fine Decoding

1 code implementation ACL 2019 Junyi Li, Wayne Xin Zhao, Ji-Rong Wen, Yang song

In this paper, we propose a novel review generation model by characterizing an elaborately designed aspect-aware coarse-to-fine generation process.

Review Generation Text Generation

A Long-Short Demands-Aware Model for Next-Item Recommendation

no code implementations12 Feb 2019 Ting Bai, Pan Du, Wayne Xin Zhao, Ji-Rong Wen, Jian-Yun Nie

Recommending the right products is the central problem in recommender systems, but the right products should also be recommended at the right time to meet the demands of users, so as to maximize their values.

Recommendation Systems

KB4Rec: A Dataset for Linking Knowledge Bases with Recommender Systems

1 code implementation30 Jul 2018 Wayne Xin Zhao, Gaole He, Hongjian Dou, Jin Huang, Siqi Ouyang, Ji-Rong Wen

Based on our linked dataset, we first preform some interesting qualitative analysis experiments, in which we discuss the effect of two important factors (i. e. popularity and recency) on whether a RS item can be linked to a KB entity.

Knowledge-Aware Recommendation

hyperdoc2vec: Distributed Representations of Hypertext Documents

1 code implementation ACL 2018 Jialong Han, Yan Song, Wayne Xin Zhao, Shuming Shi, Haisong Zhang

Hypertext documents, such as web pages and academic papers, are of great importance in delivering information in our daily life.

Citation Recommendation Document Embedding +1

Heterogeneous Information Network Embedding for Recommendation

1 code implementation29 Nov 2017 Chuan Shi, Binbin Hu, Wayne Xin Zhao, Philip S. Yu

In this paper, we propose a novel heterogeneous network embedding based approach for HIN based recommendation, called HERec.

Social and Information Networks

A General SIMD-based Approach to Accelerating Compression Algorithms

1 code implementation6 Feb 2015 Wayne Xin Zhao, Xu-Dong Zhang, Daniel Lemire, Dongdong Shan, Jian-Yun Nie, Hongfei Yan, Ji-Rong Wen

Compression algorithms are important for data oriented tasks, especially in the era of Big Data.

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