Search Results for author: Enhong Chen

Found 203 papers, 97 papers with code

TD3: Tucker Decomposition Based Dataset Distillation Method for Sequential Recommendation

1 code implementation5 Feb 2025 Jiaqing Zhang, Mingjia Yin, Hao Wang, Yawen Li, Yuyang Ye, Xingyu Lou, Junping Du, Enhong Chen

In the era of data-centric AI, the focus of recommender systems has shifted from model-centric innovations to data-centric approaches.

Dataset Distillation Meta-Learning +1

Adaptive Sampled Softmax with Inverted Multi-Index: Methods, Theory and Applications

1 code implementation15 Jan 2025 Jin Chen, Jin Zhang, Xu Huang, Yi Yang, Defu Lian, Enhong Chen

The softmax function is a cornerstone of multi-class classification, integral to a wide range of machine learning applications, from large-scale retrieval and ranking models to advanced large language models.

Multi-class Classification

Harnessing Large Language Models for Knowledge Graph Question Answering via Adaptive Multi-Aspect Retrieval-Augmentation

1 code implementation24 Dec 2024 Derong Xu, Xinhang Li, Ziheng Zhang, Zhenxi Lin, Zhihong Zhu, Zhi Zheng, Xian Wu, Xiangyu Zhao, Tong Xu, Enhong Chen

The Amar framework comprises two key sub-components: 1) a self-alignment module that aligns commonalities among entities, relations, and subgraphs to enhance retrieved text, thereby reducing noise interference; 2) a relevance gating module that employs a soft gate to learn the relevance score between question and multi-aspect retrieved data, to determine which information should be used to enhance LLMs' output, or even filtered altogether.

Graph Question Answering Hallucination +3

A Flexible Plug-and-Play Module for Generating Variable-Length

1 code implementation12 Dec 2024 Liyang He, Yuren Zhang, Rui Li, Zhenya Huang, Runze Wu, Enhong Chen

The NHL framework introduces a novel mechanism to simultaneously generate hash codes of varying lengths in a nested manner.

Deep Hashing Image Retrieval

Optimizing Sequential Recommendation Models with Scaling Laws and Approximate Entropy

no code implementations30 Nov 2024 Tingjia Shen, Hao Wang, Chuhan Wu, Jin Yao Chin, Wei Guo, Yong liu, Huifeng Guo, Defu Lian, Ruiming Tang, Enhong Chen

In response, we introduce the Performance Law for SR models, which aims to theoretically investigate and model the relationship between model performance and data quality.

Sequential Recommendation

TableTime: Reformulating Time Series Classification as Zero-Shot Table Understanding via Large Language Models

1 code implementation24 Nov 2024 Jiahao Wang, Mingyue Cheng, Qingyang Mao, Qi Liu, Feiyang Xu, Xin Li, Enhong Chen

Despite their effectiveness, we reveal that these methods conceal three inherent bottlenecks: (1) they struggle to encode temporal and channel-specific information in a lossless manner, both of which are critical components of multivariate time series; (2) it is much difficult to align the learned representation space with the semantic space of the LLMs; (3) they require task-specific retraining, which is both computationally expensive and labor-intensive.

Problem Decomposition Time Series +2

DisenTS: Disentangled Channel Evolving Pattern Modeling for Multivariate Time Series Forecasting

no code implementations30 Oct 2024 Zhiding Liu, Jiqian Yang, Qingyang Mao, Yuze Zhao, Mingyue Cheng, Zhi Li, Qi Liu, Enhong Chen

To this end, we propose DisenTS, a tailored framework for modeling disentangled channel evolving patterns in general multivariate time series forecasting.

Multivariate Time Series Forecasting Time Series

Mitigating Hallucinations of Large Language Models in Medical Information Extraction via Contrastive Decoding

no code implementations21 Oct 2024 Derong Xu, Ziheng Zhang, Zhihong Zhu, Zhenxi Lin, Qidong Liu, Xian Wu, Tong Xu, Xiangyu Zhao, Yefeng Zheng, Enhong Chen

In this paper, we address these hallucination issues in the context of Medical Information Extraction (MIE) tasks by introducing ALternate Contrastive Decoding (ALCD).

Hallucination

Learning Recommender Systems with Soft Target: A Decoupled Perspective

1 code implementation9 Oct 2024 Hao Zhang, Mingyue Cheng, Qi Liu, Yucong Luo, Rui Li, Enhong Chen

Learning recommender systems with multi-class optimization objective is a prevalent setting in recommendation.

Recommendation Systems

MDAP: A Multi-view Disentangled and Adaptive Preference Learning Framework for Cross-Domain Recommendation

1 code implementation8 Oct 2024 Junxiong Tong, Mingjia Yin, Hao Wang, Qiushi Pan, Defu Lian, Enhong Chen

Cross-domain Recommendation systems leverage multi-domain user interactions to improve performance, especially in sparse data or new user scenarios.

Decoder feature selection +1

Diffusion Auto-regressive Transformer for Effective Self-supervised Time Series Forecasting

3 code implementations8 Oct 2024 Daoyu Wang, Mingyue Cheng, Zhiding Liu, Qi Liu, Enhong Chen

Self-supervised learning has become a popular and effective approach for enhancing time series forecasting, enabling models to learn universal representations from unlabeled data.

Decoder Denoising +3

Benchmarking Large Language Models for Conversational Question Answering in Multi-instructional Documents

no code implementations1 Oct 2024 Shiwei Wu, Chen Zhang, Yan Gao, Qimeng Wang, Tong Xu, Yao Hu, Enhong Chen

Instructional documents are rich sources of knowledge for completing various tasks, yet their unique challenges in conversational question answering (CQA) have not been thoroughly explored.

Benchmarking Conversational Question Answering

Pre-trained Language Model and Knowledge Distillation for Lightweight Sequential Recommendation

no code implementations23 Sep 2024 Li Li, Mingyue Cheng, Zhiding Liu, Hao Zhang, Qi Liu, Enhong Chen

The algorithm operates in two stages: in the first stage, we fine-tune the pre-trained language model on the recommendation dataset to transfer the pre-trained knowledge to the recommendation task; in the second stage, we distill the trained language model to transfer the learned knowledge to a lightweight model.

Knowledge Distillation Language Modeling +2

ChemEval: A Comprehensive Multi-Level Chemical Evaluation for Large Language Models

1 code implementation21 Sep 2024 Yuqing Huang, Rongyang Zhang, Xuesong He, Xuyang Zhi, Hao Wang, Xin Li, Feiyang Xu, Deguang Liu, Huadong Liang, Yi Li, Jian Cui, Zimu Liu, Shijin Wang, Guoping Hu, Guiquan Liu, Qi Liu, Defu Lian, Enhong Chen

To this end, we propose \textbf{\textit{ChemEval}}, which provides a comprehensive assessment of the capabilities of LLMs across a wide range of chemical domain tasks.

Few-Shot Learning Instruction Following

Generating Event-oriented Attribution for Movies via Two-Stage Prefix-Enhanced Multimodal LLM

no code implementations14 Sep 2024 Yuanjie Lyu, Tong Xu, Zihan Niu, Bo Peng, Jing Ke, Enhong Chen

Correspondingly, in the global stage, we strengthen the connections between associated events using an inferential knowledge graph, and design an event-aware prefix that directs the model to focus on associated events rather than all preceding clips, resulting in accurate event attribution.

Revisiting the Solution of Meta KDD Cup 2024: CRAG

1 code implementation9 Sep 2024 Jie Ouyang, Yucong Luo, Mingyue Cheng, Daoyu Wang, Shuo Yu, Qi Liu, Enhong Chen

This paper presents the solution of our team APEX in the Meta KDD CUP 2024: CRAG Comprehensive RAG Benchmark Challenge.

RAG Retrieval +1

Multi-Source Knowledge Pruning for Retrieval-Augmented Generation: A Benchmark and Empirical Study

2 code implementations3 Sep 2024 Shuo Yu, Mingyue Cheng, Jiqian Yang, Jie Ouyang, Yucong Luo, Chenyi Lei, Qi Liu, Enhong Chen

Retrieval-augmented generation (RAG) is increasingly recognized as an effective approach for mitigating the hallucination of large language models (LLMs) through the integration of external knowledge.

Benchmarking Hallucination +2

ToolACE: Winning the Points of LLM Function Calling

no code implementations2 Sep 2024 Weiwen Liu, Xu Huang, Xingshan Zeng, Xinlong Hao, Shuai Yu, Dexun Li, Shuai Wang, Weinan Gan, Zhengying Liu, Yuanqing Yu, Zezhong Wang, Yuxian Wang, Wu Ning, Yutai Hou, Bin Wang, Chuhan Wu, Xinzhi Wang, Yong liu, Yasheng Wang, Duyu Tang, Dandan Tu, Lifeng Shang, Xin Jiang, Ruiming Tang, Defu Lian, Qun Liu, Enhong Chen

Function calling significantly extends the application boundary of large language models, where high-quality and diverse training data is critical for unlocking this capability.

Bridging User Dynamics: Transforming Sequential Recommendations with Schrödinger Bridge and Diffusion Models

no code implementations30 Aug 2024 Wenjia Xie, Rui Zhou, Hao Wang, Tingjia Shen, Enhong Chen

Sequential recommendation has attracted increasing attention due to its ability to accurately capture the dynamic changes in user interests.

Sequential Recommendation

VideoLLM-MoD: Efficient Video-Language Streaming with Mixture-of-Depths Vision Computation

no code implementations29 Aug 2024 Shiwei Wu, Joya Chen, Kevin Qinghong Lin, Qimeng Wang, Yan Gao, Qianli Xu, Tong Xu, Yao Hu, Enhong Chen, Mike Zheng Shou

Our method, VideoLLM-MoD, is inspired by mixture-of-depths LLMs and addresses the challenge of numerous vision tokens in long-term or streaming video.

Learning Deep Tree-based Retriever for Efficient Recommendation: Theory and Method

1 code implementation21 Aug 2024 Ze Liu, Jin Zhang, Chao Feng, Defu Lian, Jie Wang, Enhong Chen

Although advancements in deep learning have significantly enhanced the recommendation accuracy of deep recommendation models, these methods still suffer from low recommendation efficiency.

Binary Classification Multi-class Classification +1

Analytical and Empirical Study of Herding Effects in Recommendation Systems

no code implementations20 Aug 2024 Hong Xie, Mingze Zhong, Defu Lian, Zhen Wang, Enhong Chen

We also study the speed of convergence numerically and reveal trade-offs in selecting rating aggregation rules and review selection mechanisms.

Recommendation Systems

Multi-agent Multi-armed Bandits with Stochastic Sharable Arm Capacities

no code implementations20 Aug 2024 Hong Xie, Jinyu Mo, Defu Lian, Jie Wang, Enhong Chen

We also design an iterative distributed algorithm for players to commit to an optimal arm pulling profile with a constant number of rounds in expectation.

Multi-Armed Bandits

Leveraging Entity Information for Cross-Modality Correlation Learning: The Entity-Guided Multimodal Summarization

1 code implementation6 Aug 2024 Yanghai Zhang, Ye Liu, Shiwei Wu, Kai Zhang, Xukai Liu, Qi Liu, Enhong Chen

The rapid increase in multimedia data has spurred advancements in Multimodal Summarization with Multimodal Output (MSMO), which aims to produce a multimodal summary that integrates both text and relevant images.

Knowledge Distillation Language Modeling +1

Learn while Unlearn: An Iterative Unlearning Framework for Generative Language Models

no code implementations25 Jul 2024 Haoyu Tang, Ye Liu, Xukai Liu, Kai Zhang, Yanghai Zhang, Qi Liu, Enhong Chen

Recent advancements in machine learning, particularly in Natural Language Processing (NLP), have led to the development of sophisticated models trained on extensive datasets, yet raising concerns about the potential leakage of sensitive information.

Contrastive Learning Machine Unlearning

Detect, Investigate, Judge and Determine: A Novel LLM-based Framework for Few-shot Fake News Detection

no code implementations12 Jul 2024 Ye Liu, Jiajun Zhu, Kai Zhang, Haoyu Tang, Yanghai Zhang, Xukai Liu, Qi Liu, Enhong Chen

To address these shortcomings, we propose a Dual-perspective Augmented Fake News Detection (DAFND) model, designed to enhance LLMs from both inside and outside perspectives.

Fake News Detection In-Context Learning

Empowering Few-Shot Relation Extraction with The Integration of Traditional RE Methods and Large Language Models

1 code implementation12 Jul 2024 Ye Liu, Kai Zhang, Aoran Gan, Linan Yue, Feng Hu, Qi Liu, Enhong Chen

Specifically, DSARE innovatively injects the prior knowledge of LLMs into traditional RE models, and conversely enhances LLMs' task-specific aptitude for RE through relation extraction augmentation.

In-Context Learning Relation +1

Entropy Law: The Story Behind Data Compression and LLM Performance

3 code implementations9 Jul 2024 Mingjia Yin, Chuhan Wu, YuFei Wang, Hao Wang, Wei Guo, Yasheng Wang, Yong liu, Ruiming Tang, Defu Lian, Enhong Chen

Inspired by the information compression nature of LLMs, we uncover an ``entropy law'' that connects LLM performance with data compression ratio and first-epoch training loss, which reflect the information redundancy of a dataset and the mastery of inherent knowledge encoded in this dataset, respectively.

Data Compression

A Survey of Models for Cognitive Diagnosis: New Developments and Future Directions

no code implementations7 Jul 2024 Fei Wang, Weibo Gao, Qi Liu, Jiatong Li, Guanhao Zhao, Zheng Zhang, Zhenya Huang, Mengxiao Zhu, Shijin Wang, Wei Tong, Enhong Chen

Cognitive diagnosis has been developed for decades as an effective measurement tool to evaluate human cognitive status such as ability level and knowledge mastery.

cognitive diagnosis

Foundations and Frontiers of Graph Learning Theory

no code implementations3 Jul 2024 Yu Huang, Min Zhou, Menglin Yang, Zhen Wang, Muhan Zhang, Jie Wang, Hong Xie, Hao Wang, Defu Lian, Enhong Chen

Recent advancements in graph learning have revolutionized the way to understand and analyze data with complex structures.

Graph Learning Learning Theory

Retrieve-Plan-Generation: An Iterative Planning and Answering Framework for Knowledge-Intensive LLM Generation

1 code implementation21 Jun 2024 Yuanjie Lyu, Zihan Niu, Zheyong Xie, Chao Zhang, Tong Xu, Yang Wang, Enhong Chen

Despite the significant progress of large language models (LLMs) in various tasks, they often produce factual errors due to their limited internal knowledge.

Answer Generation RAG

In-Context Former: Lightning-fast Compressing Context for Large Language Model

no code implementations19 Jun 2024 Xiangfeng Wang, Zaiyi Chen, Zheyong Xie, Tong Xu, Yongyi He, Enhong Chen

With the rising popularity of Transformer-based large language models (LLMs), reducing their high inference costs has become a significant research focus.

Language Modeling Language Modelling +2

When Box Meets Graph Neural Network in Tag-aware Recommendation

1 code implementation17 Jun 2024 Fake Lin, Ziwei Zhao, Xi Zhu, Da Zhang, Shitian Shen, Xueying Li, Tong Xu, Suojuan Zhang, Enhong Chen

Last year has witnessed the re-flourishment of tag-aware recommender systems supported by the LLM-enriched tags.

Diversity Graph Neural Network +1

From a Social Cognitive Perspective: Context-aware Visual Social Relationship Recognition

no code implementations12 Jun 2024 Shiwei Wu, Chao Zhang, Joya Chen, Tong Xu, Likang Wu, Yao Hu, Enhong Chen

People's social relationships are often manifested through their surroundings, with certain objects or interactions acting as symbols for specific relationships, e. g., wedding rings, roses, hugs, or holding hands.

Descriptive Visual Social Relationship Recognition

Exploring User Retrieval Integration towards Large Language Models for Cross-Domain Sequential Recommendation

1 code implementation5 Jun 2024 Tingjia Shen, Hao Wang, Jiaqing Zhang, Sirui Zhao, Liangyue Li, Zulong Chen, Defu Lian, Enhong Chen

To this end, we propose a novel framework named URLLM, which aims to improve the CDSR performance by exploring the User Retrieval approach and domain grounding on LLM simultaneously.

Contrastive Learning Language Modelling +4

EduNLP: Towards a Unified and Modularized Library for Educational Resources

1 code implementation3 Jun 2024 Zhenya Huang, Yuting Ning, Longhu Qin, Shiwei Tong, Shangzi Xue, Tong Xiao, Xin Lin, Jiayu Liu, Qi Liu, Enhong Chen, Shijing Wang

We also provide a configurable pipeline to unify the data usage and model usage in standard ways, where users can customize their own needs.

Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis

1 code implementation31 May 2024 Chaoyou Fu, Yuhan Dai, Yongdong Luo, Lei LI, Shuhuai Ren, Renrui Zhang, Zihan Wang, Chenyu Zhou, Yunhang Shen, Mengdan Zhang, Peixian Chen, Yanwei Li, Shaohui Lin, Sirui Zhao, Ke Li, Tong Xu, Xiawu Zheng, Enhong Chen, Rongrong Ji, Xing Sun

With Video-MME, we extensively evaluate various state-of-the-art MLLMs, including GPT-4 series and Gemini 1. 5 Pro, as well as open-source image models like InternVL-Chat-V1. 5 and video models like LLaVA-NeXT-Video.

MME Video MME

Cognitive Evolutionary Learning to Select Feature Interactions for Recommender Systems

no code implementations29 May 2024 Runlong Yu, Qixiang Shao, Qi Liu, Huan Liu, Enhong Chen

We show that CELL can adaptively evolve into different models for different tasks and data, which enables practitioners to access off-the-shelf models.

Recommendation Systems

Dataset Regeneration for Sequential Recommendation

1 code implementation28 May 2024 Mingjia Yin, Hao Wang, Wei Guo, Yong liu, Suojuan Zhang, Sirui Zhao, Defu Lian, Enhong Chen

The sequential recommender (SR) system is a crucial component of modern recommender systems, as it aims to capture the evolving preferences of users.

Sequential Recommendation

NoteLLM-2: Multimodal Large Representation Models for Recommendation

1 code implementation27 May 2024 Chao Zhang, Haoxin Zhang, Shiwei Wu, Di wu, Tong Xu, Xiangyu Zhao, Yan Gao, Yao Hu, Enhong Chen

While leveraging existing Multimodal Large Language Models (MLLMs) for such tasks is promising, challenges arise due to their delayed release compared to corresponding LLMs and the inefficiency in representation tasks.

In-Context Learning

Learning Partially Aligned Item Representation for Cross-Domain Sequential Recommendation

no code implementations21 May 2024 Mingjia Yin, Hao Wang, Wei Guo, Yong liu, Zhi Li, Sirui Zhao, Zhen Wang, Defu Lian, Enhong Chen

Cross-domain sequential recommendation (CDSR) aims to uncover and transfer users' sequential preferences across multiple recommendation domains.

Multi-Task Learning Self-Supervised Learning +1

Knowledge Graph Pruning for Recommendation

no code implementations19 May 2024 Fake Lin, Xi Zhu, Ziwei Zhao, Deqiang Huang, Yu Yu, Xueying Li, Zhi Zheng, Tong Xu, Enhong Chen

Recent years have witnessed the prosperity of knowledge graph based recommendation system (KGRS), which enriches the representation of users, items, and entities by structural knowledge with striking improvement.

Graph Neural Network

DynLLM: When Large Language Models Meet Dynamic Graph Recommendation

no code implementations13 May 2024 Ziwei Zhao, Fake Lin, Xi Zhu, Zhi Zheng, Tong Xu, Shitian Shen, Xueying Li, Zikai Yin, Enhong Chen

To bridge this gap, in this paper, we propose a novel framework, called DynLLM, to deal with the dynamic graph recommendation task with LLMs.

Graph Embedding Recommendation Systems

Learning to Solve Geometry Problems via Simulating Human Dual-Reasoning Process

1 code implementation10 May 2024 Tong Xiao, Jiayu Liu, Zhenya Huang, Jinze Wu, Jing Sha, Shijin Wang, Enhong Chen

Knowledge System controls an implicit reasoning process, which is responsible for providing diagram information and geometry knowledge according to a step-wise reasoning goal generated by Inference System.

Geometry Problem Solving Machine Translation +1

Understanding Privacy Risks of Embeddings Induced by Large Language Models

no code implementations25 Apr 2024 Zhihao Zhu, Ninglu Shao, Defu Lian, Chenwang Wu, Zheng Liu, Yi Yang, Enhong Chen

Large language models (LLMs) show early signs of artificial general intelligence but struggle with hallucinations.

Retrieval

WESE: Weak Exploration to Strong Exploitation for LLM Agents

no code implementations11 Apr 2024 Xu Huang, Weiwen Liu, Xiaolong Chen, Xingmei Wang, Defu Lian, Yasheng Wang, Ruiming Tang, Enhong Chen

Concretely, WESE involves decoupling the exploration and exploitation process, employing a cost-effective weak agent to perform exploration tasks for global knowledge.

Decision Making Prompt Engineering

Survey of Computerized Adaptive Testing: A Machine Learning Perspective

1 code implementation31 Mar 2024 Qi Liu, Yan Zhuang, Haoyang Bi, Zhenya Huang, Weizhe Huang, Jiatong Li, Junhao Yu, Zirui Liu, Zirui Hu, Yuting Hong, Zachary A. Pardos, Haiping Ma, Mengxiao Zhu, Shijin Wang, Enhong Chen

Computerized Adaptive Testing (CAT) provides an efficient and tailored method for assessing the proficiency of examinees, by dynamically adjusting test questions based on their performance.

cognitive diagnosis Question Selection +2

END4Rec: Efficient Noise-Decoupling for Multi-Behavior Sequential Recommendation

no code implementations26 Mar 2024 Yongqiang Han, Hao Wang, Kefan Wang, Likang Wu, Zhi Li, Wei Guo, Yong liu, Defu Lian, Enhong Chen

In recommendation systems, users frequently engage in multiple types of behaviors, such as clicking, adding to a cart, and purchasing.

Denoising Sequential Recommendation +1

Locating and Mitigating Gender Bias in Large Language Models

no code implementations21 Mar 2024 Yuchen Cai, Ding Cao, Rongxi Guo, Yaqin Wen, Guiquan Liu, Enhong Chen

Prior research has typically tackled the issue of bias through a one-dimensional perspective, concentrating either on locating or mitigating it.

knowledge editing Language Modelling +2

Editing Knowledge Representation of Language Model via Rephrased Prefix Prompts

no code implementations21 Mar 2024 Yuchen Cai, Ding Cao, Rongxi Guo, Yaqin Wen, Guiquan Liu, Enhong Chen

The results indicate that PSPEM can serve as an alternative to original prompts, supporting the model in effective editing.

Attribute knowledge editing +3

Towards Personalized Evaluation of Large Language Models with An Anonymous Crowd-Sourcing Platform

1 code implementation13 Mar 2024 Mingyue Cheng, Hao Zhang, Jiqian Yang, Qi Liu, Li Li, Xin Huang, Liwei Song, Zhi Li, Zhenya Huang, Enhong Chen

Through this gateway, users have the opportunity to submit their questions, testing the models on a personalized and potentially broader range of capabilities.

Language Modelling Large Language Model

Empowering Sequential Recommendation from Collaborative Signals and Semantic Relatedness

1 code implementation12 Mar 2024 Mingyue Cheng, Hao Zhang, Qi Liu, Fajie Yuan, Zhi Li, Zhenya Huang, Enhong Chen, Jun Zhou, Longfei Li

It is also significant to model the \textit{semantic relatedness} reflected in content features, e. g., images and text.

Sequential Recommendation

A Dataset for the Validation of Truth Inference Algorithms Suitable for Online Deployment

1 code implementation10 Mar 2024 Fei Wang, Haoyu Liu, Haoyang Bi, Xiangzhuang Shen, Renyu Zhu, Runze Wu, Minmin Lin, Tangjie Lv, Changjie Fan, Qi Liu, Zhenya Huang, Enhong Chen

In this paper, we introduce a substantial crowdsourcing annotation dataset collected from a real-world crowdsourcing platform.

Bit-mask Robust Contrastive Knowledge Distillation for Unsupervised Semantic Hashing

1 code implementation10 Mar 2024 Liyang He, Zhenya Huang, Jiayu Liu, Enhong Chen, Fei Wang, Jing Sha, Shijin Wang

In this paper, we propose an innovative Bit-mask Robust Contrastive knowledge Distillation (BRCD) method, specifically devised for the distillation of semantic hashing models.

Image Retrieval Knowledge Distillation +1

Unified Uncertainty Estimation for Cognitive Diagnosis Models

no code implementations9 Mar 2024 Fei Wang, Qi Liu, Enhong Chen, Chuanren Liu, Zhenya Huang, Jinze Wu, Shijin Wang

Specifically, based on the idea of estimating the posterior distributions of cognitive diagnosis model parameters, we first provide a unified objective function for mini-batch based optimization that can be more efficiently applied to a wide range of models and large datasets.

cognitive diagnosis

Multi-perspective Improvement of Knowledge Graph Completion with Large Language Models

1 code implementation4 Mar 2024 Derong Xu, Ziheng Zhang, Zhenxi Lin, Xian Wu, Zhihong Zhu, Tong Xu, Xiangyu Zhao, Yefeng Zheng, Enhong Chen

Knowledge graph completion (KGC) is a widely used method to tackle incompleteness in knowledge graphs (KGs) by making predictions for missing links.

Link Prediction Relation +1

NoteLLM: A Retrievable Large Language Model for Note Recommendation

no code implementations4 Mar 2024 Chao Zhang, Shiwei Wu, Haoxin Zhang, Tong Xu, Yan Gao, Yao Hu, Di wu, Enhong Chen

Indeed, learning to generate hashtags/categories can potentially enhance note embeddings, both of which compress key note information into limited content.

Contrastive Learning Language Modeling +2

Editing Factual Knowledge and Explanatory Ability of Medical Large Language Models

1 code implementation28 Feb 2024 Derong Xu, Ziheng Zhang, Zhihong Zhu, Zhenxi Lin, Qidong Liu, Xian Wu, Tong Xu, Wanyu Wang, Yuyang Ye, Xiangyu Zhao, Enhong Chen, Yefeng Zheng

To evaluate the editing impact on the behaviours of LLMs, we propose two model editing studies for medical domain: (1) editing factual knowledge for medical specialization and (2) editing the explanatory ability for complex knowledge.

Benchmarking Hallucination +1

Federated Contextual Cascading Bandits with Asynchronous Communication and Heterogeneous Users

no code implementations26 Feb 2024 Hantao Yang, Xutong Liu, Zhiyong Wang, Hong Xie, John C. S. Lui, Defu Lian, Enhong Chen

We study the problem of federated contextual combinatorial cascading bandits, where $|\mathcal{U}|$ agents collaborate under the coordination of a central server to provide tailored recommendations to the $|\mathcal{U}|$ corresponding users.

Communication-Efficient Distributed Learning with Local Immediate Error Compensation

no code implementations19 Feb 2024 Yifei Cheng, Li Shen, Linli Xu, Xun Qian, Shiwei Wu, Yiming Zhou, Tie Zhang, DaCheng Tao, Enhong Chen

However, existing compression methods either perform only unidirectional compression in one iteration with higher communication cost, or bidirectional compression with slower convergence rate.

Seed Optimization with Frozen Generator for Superior Zero-shot Low-light Enhancement

no code implementations15 Feb 2024 Yuxuan Gu, Yi Jin, Ben Wang, Zhixiang Wei, Xiaoxiao Ma, Pengyang Ling, Haoxuan Wang, Huaian Chen, Enhong Chen

In this work, we observe that the generators, which are pre-trained on massive natural images, inherently hold the promising potential for superior low-light image enhancement against varying scenarios. Specifically, we embed a pre-trained generator to Retinex model to produce reflectance maps with enhanced detail and vividness, thereby recovering features degraded by low-light conditions. Taking one step further, we introduce a novel optimization strategy, which backpropagates the gradients to the input seeds rather than the parameters of the low-light enhancement model, thus intactly retaining the generative knowledge learned from natural images and achieving faster convergence speed.

Low-Light Image Enhancement

Understanding the planning of LLM agents: A survey

no code implementations5 Feb 2024 Xu Huang, Weiwen Liu, Xiaolong Chen, Xingmei Wang, Hao Wang, Defu Lian, Yasheng Wang, Ruiming Tang, Enhong Chen

As Large Language Models (LLMs) have shown significant intelligence, the progress to leverage LLMs as planning modules of autonomous agents has attracted more attention.

Survey

Securing Recommender System via Cooperative Training

1 code implementation23 Jan 2024 Qingyang Wang, Chenwang Wu, Defu Lian, Enhong Chen

Consequently, we put forth a Game-based Co-training Attack (GCoAttack), which frames the proposed CoAttack and TCD as a game-theoretic process, thoroughly exploring CoAttack's attack potential in the cooperative training of attack and defense.

Recommendation Systems

Communication-Efficient Personalized Federated Learning for Speech-to-Text Tasks

no code implementations18 Jan 2024 Yichao Du, Zhirui Zhang, Linan Yue, Xu Huang, Yuqing Zhang, Tong Xu, Linli Xu, Enhong Chen

To protect privacy and meet legal regulations, federated learning (FL) has gained significant attention for training speech-to-text (S2T) systems, including automatic speech recognition (ASR) and speech translation (ST).

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

Large Language Models for Generative Information Extraction: A Survey

1 code implementation29 Dec 2023 Derong Xu, Wei Chen, Wenjun Peng, Chao Zhang, Tong Xu, Xiangyu Zhao, Xian Wu, Yefeng Zheng, Yang Wang, Enhong Chen

Information extraction (IE) aims to extract structural knowledge from plain natural language texts.

Survey

Group Multi-View Transformer for 3D Shape Analysis with Spatial Encoding

1 code implementation27 Dec 2023 Lixiang Xu, Qingzhe Cui, Richang Hong, Wei Xu, Enhong Chen, Xin Yuan, Chenglong Li, Yuanyan Tang

The large model GMViT achieves excellent 3D classification and retrieval results on the benchmark datasets ModelNet, ShapeNetCore55, and MCB.

3D Classification 3D Shape Recognition +2

Unlocking the Potential of Large Language Models for Explainable Recommendations

1 code implementation25 Dec 2023 Yucong Luo, Mingyue Cheng, Hao Zhang, Junyu Lu, Qi Liu, Enhong Chen

In this study, we propose LLMXRec, a simple yet effective two-stage explainable recommendation framework aimed at further boosting the explanation quality by employing LLMs.

Decision Making Explainable Recommendation +2

Model Stealing Attack against Recommender System

no code implementations18 Dec 2023 Zhihao Zhu, Rui Fan, Chenwang Wu, Yi Yang, Defu Lian, Enhong Chen

Some adversarial attacks have achieved model stealing attacks against recommender systems, to some extent, by collecting abundant training data of the target model (target data) or making a mass of queries.

model Recommendation Systems

Model Stealing Attack against Graph Classification with Authenticity, Uncertainty and Diversity

no code implementations18 Dec 2023 Zhihao Zhu, Chenwang Wu, Rui Fan, Yi Yang, Zhen Wang, Defu Lian, Enhong Chen

Recent research demonstrates that GNNs are vulnerable to the model stealing attack, a nefarious endeavor geared towards duplicating the target model via query permissions.

Active Learning Diversity +2

Large Language Model based Long-tail Query Rewriting in Taobao Search

1 code implementation7 Nov 2023 Wenjun Peng, Guiyang Li, Yue Jiang, Zilong Wang, Dan Ou, Xiaoyi Zeng, Derong Xu, Tong Xu, Enhong Chen

In the realm of e-commerce search, the significance of semantic matching cannot be overstated, as it directly impacts both user experience and company revenue.

Contrastive Learning Language Modeling +3

APGL4SR: A Generic Framework with Adaptive and Personalized Global Collaborative Information in Sequential Recommendation

1 code implementation6 Nov 2023 Mingjia Yin, Hao Wang, Xiang Xu, Likang Wu, Sirui Zhao, Wei Guo, Yong liu, Ruiming Tang, Defu Lian, Enhong Chen

To this end, we propose a graph-driven framework, named Adaptive and Personalized Graph Learning for Sequential Recommendation (APGL4SR), that incorporates adaptive and personalized global collaborative information into sequential recommendation systems.

Graph Learning Multi-Task Learning +1

Generative Input: Towards Next-Generation Input Methods Paradigm

no code implementations2 Nov 2023 Keyu Ding, Yongcan Wang, Zihang Xu, Zhenzhen Jia, Shijin Wang, Cong Liu, Enhong Chen

The results demonstrate that we have achieved state-of-the-art performance for the first time in the Full-mode Key-sequence to Characters(FK2C) task.

AutoSAM: Towards Automatic Sampling of User Behaviors for Sequential Recommender Systems

1 code implementation1 Nov 2023 Hao Zhang, Mingyue Cheng, Qi Liu, Zhiding Liu, Junzhe Jiang, Enhong Chen

Sequential recommender systems (SRS) have gained widespread popularity in recommendation due to their ability to effectively capture dynamic user preferences.

Future prediction Sequential Recommendation

Woodpecker: Hallucination Correction for Multimodal Large Language Models

1 code implementation24 Oct 2023 Shukang Yin, Chaoyou Fu, Sirui Zhao, Tong Xu, Hao Wang, Dianbo Sui, Yunhang Shen, Ke Li, Xing Sun, Enhong Chen

Hallucination is a big shadow hanging over the rapidly evolving Multimodal Large Language Models (MLLMs), referring to the phenomenon that the generated text is inconsistent with the image content.

Hallucination

Bi-discriminator Domain Adversarial Neural Networks with Class-Level Gradient Alignment

1 code implementation21 Oct 2023 Chuang Zhao, Hongke Zhao, HengShu Zhu, Zhenya Huang, Nan Feng, Enhong Chen, Hui Xiong

One prevalent solution is the bi-discriminator domain adversarial network, which strives to identify target domain samples outside the support of the source domain distribution and enforces their classification to be consistent on both discriminators.

Contrastive Learning Learning Theory +1

HEProto: A Hierarchical Enhancing ProtoNet based on Multi-Task Learning for Few-shot Named Entity Recognition

1 code implementation CIKM 2023 Wei Chen, Lili Zhao, Pengfei Luo, Tong Xu, Yi Zheng, Enhong Chen

Great efforts have been made on this task with competitive performance, however, they usually treat the two subtasks, namely span detection and type classification, as mutually independent, and the integrity and correlation between subtasks have been largely ignored.

Contrastive Learning Few-shot NER +4

Large-Scale OD Matrix Estimation with A Deep Learning Method

no code implementations9 Oct 2023 Zheli Xiong, Defu Lian, Enhong Chen, Gang Chen, Xiaomin Cheng

To alleviate this problem, some researchers incorporate a prior OD matrix as a target in the regression to provide more structural constraints.

Deep Learning

Interactive Graph Convolutional Filtering

no code implementations4 Sep 2023 Jin Zhang, Defu Lian, Hong Xie, Yawen Li, Enhong Chen

Furthermore, we employ Bayesian meta-learning methods to effectively address the cold-start problem and derive theoretical regret bounds for our proposed method, ensuring a robust performance guarantee.

Collaborative Filtering Meta-Learning +2

KMF: Knowledge-Aware Multi-Faceted Representation Learning for Zero-Shot Node Classification

no code implementations15 Aug 2023 Likang Wu, Junji Jiang, Hongke Zhao, Hao Wang, Defu Lian, Mengdi Zhang, Enhong Chen

However, the multi-faceted semantic orientation in the feature-semantic alignment has been neglected by previous work, i. e. the content of a node usually covers diverse topics that are relevant to the semantics of multiple labels.

Node Classification Representation Learning +1

Multi-Grained Multimodal Interaction Network for Entity Linking

1 code implementation19 Jul 2023 Pengfei Luo, Tong Xu, Shiwei Wu, Chen Zhu, Linli Xu, Enhong Chen

Then, to derive the similarity matching score for each mention-entity pair, we device three interaction units to comprehensively explore the intra-modal interaction and inter-modal fusion among features of entities and mentions.

Contrastive Learning Descriptive +2

A DeepLearning Framework for Dynamic Estimation of Origin-Destination Sequence

no code implementations11 Jul 2023 Zheli Xiong, Defu Lian, Enhong Chen, Gang Chen, Xiaomin Cheng

To this end, this paper proposes an integrated method, which uses deep learning methods to infer the structure of OD sequence and uses structural constraints to guide traditional numerical optimization.

Exploring Large Language Model for Graph Data Understanding in Online Job Recommendations

1 code implementation10 Jul 2023 Likang Wu, Zhaopeng Qiu, Zhi Zheng, HengShu Zhu, Enhong Chen

This paper focuses on unveiling the capability of large language models in understanding behavior graphs and leveraging this understanding to enhance recommendations in online recruitment, including the promotion of out-of-distribution (OOD) application.

Language Modeling Language Modelling +2

A Solution to CVPR'2023 AQTC Challenge: Video Alignment for Multi-Step Inference

1 code implementation26 Jun 2023 Chao Zhang, Shiwei Wu, Sirui Zhao, Tong Xu, Enhong Chen

In this paper, we present a solution for enhancing video alignment to improve multi-step inference.

Video Alignment

A Survey on Multimodal Large Language Models

1 code implementation23 Jun 2023 Shukang Yin, Chaoyou Fu, Sirui Zhao, Ke Li, Xing Sun, Tong Xu, Enhong Chen

Recently, Multimodal Large Language Model (MLLM) represented by GPT-4V has been a new rising research hotspot, which uses powerful Large Language Models (LLMs) as a brain to perform multimodal tasks.

Hallucination In-Context Learning +8

Geometric Deep Learning for Structure-Based Drug Design: A Survey

1 code implementation20 Jun 2023 Zaixi Zhang, Jiaxian Yan, Yining Huang, Qi Liu, Enhong Chen, Mengdi Wang, Marinka Zitnik

Structure-based drug design (SBDD) leverages the three-dimensional geometry of proteins to identify potential drug candidates.

Benchmarking Deep Learning +3

From Static Benchmarks to Adaptive Testing: Psychometrics in AI Evaluation

1 code implementation18 Jun 2023 Yan Zhuang, Qi Liu, Yuting Ning, Weizhe Huang, Zachary A. Pardos, Patrick C. Kyllonen, Jiyun Zu, Qingyang Mao, Rui Lv, Zhenya Huang, Guanhao Zhao, Zheng Zhang, Shijin Wang, Enhong Chen

As AI systems continue to grow, particularly generative models like Large Language Models (LLMs), their rigorous evaluation is crucial for development and deployment.

Mathematical Reasoning

Description-Enhanced Label Embedding Contrastive Learning for Text Classification

1 code implementation15 Jun 2023 Kun Zhang, Le Wu, Guangyi Lv, Enhong Chen, Shulan Ruan, Jing Liu, Zhiqiang Zhang, Jun Zhou, Meng Wang

Then, we propose a novel Relation of Relation Learning Network (R2-Net) for text classification, in which text classification and R2 classification are treated as optimization targets.

Contrastive Learning Relation +4

Multi-Temporal Relationship Inference in Urban Areas

1 code implementation15 Jun 2023 Shuangli Li, Jingbo Zhou, Ji Liu, Tong Xu, Enhong Chen, Hui Xiong

Specifically, we propose a solution to Trial with a graph learning scheme, which includes a spatially evolving graph neural network (SEENet) with two collaborative components: spatially evolving graph convolution module (SEConv) and spatially evolving self-supervised learning strategy (SE-SSL).

Graph Learning Graph Neural Network +2

Recognizing Unseen Objects via Multimodal Intensive Knowledge Graph Propagation

no code implementations14 Jun 2023 Likang Wu, Zhi Li, Hongke Zhao, Zhefeng Wang, Qi Liu, Baoxing Huai, Nicholas Jing Yuan, Enhong Chen

Zero-Shot Learning (ZSL), which aims at automatically recognizing unseen objects, is a promising learning paradigm to understand new real-world knowledge for machines continuously.

Attribute Knowledge Graphs +2

A Survey on Large Language Models for Recommendation

2 code implementations31 May 2023 Likang Wu, Zhi Zheng, Zhaopeng Qiu, Hao Wang, Hongchao Gu, Tingjia Shen, Chuan Qin, Chen Zhu, HengShu Zhu, Qi Liu, Hui Xiong, Enhong Chen

Large Language Models (LLMs) have emerged as powerful tools in the field of Natural Language Processing (NLP) and have recently gained significant attention in the domain of Recommendation Systems (RS).

Recommendation Systems +2

An Equivariant Generative Framework for Molecular Graph-Structure Co-Design

1 code implementation12 Apr 2023 Zaixi Zhang, Qi Liu, Chee-Kong Lee, Chang-Yu Hsieh, Enhong Chen

Our extensive investigation reveals that the 2D topology and 3D geometry contain intrinsically complementary information in molecule design, and provide new insights into machine learning-based molecule representation and generation.

3D geometry Drug Design +3

Quiz-based Knowledge Tracing

no code implementations5 Apr 2023 Shuanghong Shen, Enhong Chen, Bihan Xu, Qi Liu, Zhenya Huang, Linbo Zhu, Yu Su

In this paper, we present the Quiz-based Knowledge Tracing (QKT) model to monitor students' knowledge states according to their quiz-based learning interactions.

Decision Making Knowledge Tracing

Provably Convergent Subgraph-wise Sampling for Fast GNN Training

no code implementations17 Mar 2023 Jie Wang, Zhihao Shi, Xize Liang, Defu Lian, Shuiwang Ji, Bin Li, Enhong Chen, Feng Wu

During the message passing (MP) in GNNs, subgraph-wise sampling methods discard messages outside the mini-batches in backward passes to avoid the well-known neighbor explosion problem, i. e., the exponentially increasing dependencies of nodes with the number of MP iterations.

AU-aware graph convolutional network for Macro- and Micro-expression spotting

1 code implementation16 Mar 2023 Shukang Yin, Shiwei Wu, Tong Xu, Shifeng Liu, Sirui Zhao, Enhong Chen

Automatic Micro-Expression (ME) spotting in long videos is a crucial step in ME analysis but also a challenging task due to the short duration and low intensity of MEs.

Micro-Expression Spotting

TimeMAE: Self-Supervised Representations of Time Series with Decoupled Masked Autoencoders

5 code implementations1 Mar 2023 Mingyue Cheng, Qi Liu, Zhiding Liu, Hao Zhang, Rujiao Zhang, Enhong Chen

In this work, we propose TimeMAE, a novel self-supervised paradigm for learning transferrable time series representations based on transformer networks.

Time Series Time Series Analysis +1

GUESR: A Global Unsupervised Data-Enhancement with Bucket-Cluster Sampling for Sequential Recommendation

no code implementations1 Mar 2023 Yongqiang Han, Likang Wu, Hao Wang, Guifeng Wang, Mengdi Zhang, Zhi Li, Defu Lian, Enhong Chen

Sequential Recommendation is a widely studied paradigm for learning users' dynamic interests from historical interactions for predicting the next potential item.

Contrastive Learning Sequential Recommendation

Federated Nearest Neighbor Machine Translation

no code implementations23 Feb 2023 Yichao Du, Zhirui Zhang, Bingzhe Wu, Lemao Liu, Tong Xu, Enhong Chen

To protect user privacy and meet legal regulations, federated learning (FL) is attracting significant attention.

Federated Learning Machine Translation +4

FormerTime: Hierarchical Multi-Scale Representations for Multivariate Time Series Classification

3 code implementations20 Feb 2023 Mingyue Cheng, Qi Liu, Zhiding Liu, Zhi Li, Yucong Luo, Enhong Chen

Deep learning-based algorithms, e. g., convolutional networks, have significantly facilitated multivariate time series classification (MTSC) task.

Time Series Time Series Analysis +1

A Novel Approach for Auto-Formulation of Optimization Problems

no code implementations9 Feb 2023 Yuting Ning, Jiayu Liu, Longhu Qin, Tong Xiao, Shangzi Xue, Zhenya Huang, Qi Liu, Enhong Chen, Jinze Wu

In the Natural Language for Optimization (NL4Opt) NeurIPS 2022 competition, competitors focus on improving the accessibility and usability of optimization solvers, with the aim of subtask 1: recognizing the semantic entities that correspond to the components of the optimization problem; subtask 2: generating formulations for the optimization problem.

Ensemble Learning named-entity-recognition +2

Towards a Holistic Understanding of Mathematical Questions with Contrastive Pre-training

1 code implementation18 Jan 2023 Yuting Ning, Zhenya Huang, Xin Lin, Enhong Chen, Shiwei Tong, Zheng Gong, Shijin Wang

To this end, in this paper, we propose a novel contrastive pre-training approach for mathematical question representations, namely QuesCo, which attempts to bring questions with more similar purposes closer.

Contrastive Learning

DFME: A New Benchmark for Dynamic Facial Micro-expression Recognition

no code implementations3 Jan 2023 Sirui Zhao, Huaying Tang, Xinglong Mao, Shifeng Liu, Yiming Zhang, Hao Wang, Tong Xu, Enhong Chen

One of the most important subconscious reactions, micro-expression (ME), is a spontaneous, subtle, and transient facial expression that reveals human beings' genuine emotion.

Emotion Classification Micro Expression Recognition +1

Resisting Graph Adversarial Attack via Cooperative Homophilous Augmentation

no code implementations15 Nov 2022 Zhihao Zhu, Chenwang Wu, Min Zhou, Hao Liao, Defu Lian, Enhong Chen

Recent studies show that Graph Neural Networks(GNNs) are vulnerable and easily fooled by small perturbations, which has raised considerable concerns for adapting GNNs in various safety-critical applications.

Adversarial Attack

Nested Named Entity Recognition from Medical Texts: An Adaptive Shared Network Architecture with Attentive CRF

no code implementations9 Nov 2022 Junzhe Jiang, Mingyue Cheng, Qi Liu, Zhi Li, Enhong Chen

Recognizing useful named entities plays a vital role in medical information processing, which helps drive the development of medical area research.

Medical Named Entity Recognition named-entity-recognition +3

One Person, One Model--Learning Compound Router for Sequential Recommendation

1 code implementation5 Nov 2022 Zhiding Liu, Mingyue Cheng, Zhi Li, Qi Liu, Enhong Chen

The core idea of CANet is to route the input user behaviors with a light-weighted router module.

Sequential Recommendation