Search Results for author: Zhi Zheng

Found 25 papers, 11 papers with code

Densing Law of LLMs

no code implementations5 Dec 2024 Chaojun Xiao, Jie Cai, Weilin Zhao, Guoyang Zeng, Biyuan Lin, Jie zhou, Zhi Zheng, Xu Han, Zhiyuan Liu, Maosong Sun

This paper introduces the concept of ``\textit{capacity density}'' as a new metric to evaluate the quality of the LLMs across different scales and describes the trend of LLMs in terms of both effectiveness and efficiency.

CaDA: Cross-Problem Routing Solver with Constraint-Aware Dual-Attention

no code implementations30 Nov 2024 Han Li, Fei Liu, Zhi Zheng, Yu Zhang, Zhenkun Wang

Recently, Neural Combinatorial Optimization (NCO), which involves training deep learning models on extensive data to learn vehicle routing heuristics, has emerged as a promising approach due to its efficiency and the reduced need for manual algorithm design.

Combinatorial Optimization Representation Learning

MiniCPM-V: A GPT-4V Level MLLM on Your Phone

2 code implementations3 Aug 2024 Yuan YAO, Tianyu Yu, Ao Zhang, Chongyi Wang, Junbo Cui, Hongji Zhu, Tianchi Cai, Haoyu Li, Weilin Zhao, Zhihui He, Qianyu Chen, Huarong Zhou, Zhensheng Zou, Haoye Zhang, Shengding Hu, Zhi Zheng, Jie zhou, Jie Cai, Xu Han, Guoyang Zeng, Dahai Li, Zhiyuan Liu, Maosong Sun

The recent surge of Multimodal Large Language Models (MLLMs) has fundamentally reshaped the landscape of AI research and industry, shedding light on a promising path toward the next AI milestone.

Hallucination Multiple-choice +3

An Adaptive System for Wearable Devices to Detect Stress Using Physiological Signals

no code implementations21 Jul 2024 Gelei Xu, Ruiyang Qin, Zhi Zheng, Yiyu Shi

Timely stress detection is crucial for protecting vulnerable groups from long-term detrimental effects by enabling early intervention.

UDC: A Unified Neural Divide-and-Conquer Framework for Large-Scale Combinatorial Optimization Problems

2 code implementations29 Jun 2024 Zhi Zheng, Changliang Zhou, Tong Xialiang, Mingxuan Yuan, Zhenkun Wang

Employing a high-efficiency Graph Neural Network (GNN) for global instance dividing and a fixed-length sub-path solver for conquering divided sub-problems, the proposed UDC framework demonstrates extensive applicability, achieving superior performance in 10 representative large-scale CO problems.

Combinatorial Optimization Graph Neural Network

Evaluating Uncertainty-based Failure Detection for Closed-Loop LLM Planners

no code implementations1 Jun 2024 Zhi Zheng, Qian Feng, Hang Li, Alois Knoll, Jianxiang Feng

As a general-purpose reasoning machine, LLMs or Multimodal Large Language Models (MLLMs) are promising for detecting failures.

Safe Multi-Agent Reinforcement Learning with Bilevel Optimization in Autonomous Driving

1 code implementation28 May 2024 Zhi Zheng, Shangding Gu

Ensuring safety in MARL, particularly when deploying it in real-world applications such as autonomous driving, emerges as a critical challenge.

Autonomous Driving Bilevel Optimization +3

DPN: Decoupling Partition and Navigation for Neural Solvers of Min-max Vehicle Routing Problems

1 code implementation27 May 2024 Zhi Zheng, Shunyu Yao, Zhenkun Wang, Xialiang Tong, Mingxuan Yuan, Ke Tang

The min-max vehicle routing problem (min-max VRP) traverses all given customers by assigning several routes and aims to minimize the length of the longest route.

Reinforcement Learning (RL)

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

Make Large Language Model a Better Ranker

no code implementations28 Mar 2024 Wen-Shuo Chao, Zhi Zheng, HengShu Zhu, Hao liu

Moreover, these LLM-based methods struggle to effectively address the order relation among candidates, particularly given the scale of ratings.

Language Modelling Large Language Model +2

Dynamic Sparse Learning: A Novel Paradigm for Efficient Recommendation

no code implementations5 Feb 2024 Shuyao Wang, Yongduo Sui, Jiancan Wu, Zhi Zheng, Hui Xiong

In the realm of deep learning-based recommendation systems, the increasing computational demands, driven by the growing number of users and items, pose a significant challenge to practical deployment.

Model Compression Recommendation Systems +1

A Cross-View Hierarchical Graph Learning Hypernetwork for Skill Demand-Supply Joint Prediction

1 code implementation31 Jan 2024 Wenshuo Chao, Zhaopeng Qiu, Likang Wu, Zhuoning Guo, Zhi Zheng, HengShu Zhu, Hao liu

The rapidly changing landscape of technology and industries leads to dynamic skill requirements, making it crucial for employees and employers to anticipate such shifts to maintain a competitive edge in the labor market.

Decoder Graph Learning +2

MindShift: Leveraging Large Language Models for Mental-States-Based Problematic Smartphone Use Intervention

no code implementations28 Sep 2023 Ruolan Wu, Chun Yu, Xiaole Pan, Yujia Liu, Ningning Zhang, Yue Fu, YuHan Wang, Zhi Zheng, Li Chen, Qiaolei Jiang, Xuhai Xu, Yuanchun Shi

We first conducted a Wizard-of-Oz study (N=12) and an interview study (N=10) to summarize the mental states behind problematic smartphone use: boredom, stress, and inertia.

Persuasion Strategies

QASnowball: An Iterative Bootstrapping Framework for High-Quality Question-Answering Data Generation

no code implementations19 Sep 2023 Kunlun Zhu, Shihao Liang, Xu Han, Zhi Zheng, Guoyang Zeng, Zhiyuan Liu, Maosong Sun

Recent years have witnessed the success of question answering (QA), especially its potential to be a foundation paradigm for tackling diverse NLP tasks.

Data Augmentation Question Answering

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 Modelling Large Language Model +1

Generative Job Recommendations with Large Language Model

no code implementations5 Jul 2023 Zhi Zheng, Zhaopeng Qiu, Xiao Hu, Likang Wu, HengShu Zhu, Hui Xiong

The rapid development of online recruitment services has encouraged the utilization of recommender systems to streamline the job seeking process.

Collaborative Filtering Language Modelling +3

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

Enhancing Chat Language Models by Scaling High-quality Instructional Conversations

1 code implementation23 May 2023 Ning Ding, Yulin Chen, Bokai Xu, Yujia Qin, Zhi Zheng, Shengding Hu, Zhiyuan Liu, Maosong Sun, BoWen Zhou

Fine-tuning on instruction data has been widely validated as an effective practice for implementing chat language models like ChatGPT.

Diversity

Cannot find the paper you are looking for? You can Submit a new open access paper.