Search Results for author:
Found 25 papers, 11 papers with code
Densing Law of LLMs
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
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.
Harnessing Multimodal Large Language Models for Multimodal Sequential Recommendation
Recent advances in Large Language Models (LLMs) have demonstrated significant potential in the field of Recommendation Systems (RSs).
MiniCPM-V: A GPT-4V Level MLLM on Your Phone
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.
Ranked #6 on
Zero-Shot Video Question Answer
on Video-MME
An Adaptive System for Wearable Devices to Detect Stress Using Physiological Signals
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
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.
Evaluating Uncertainty-based Failure Detection for Closed-Loop LLM Planners
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
Ensuring safety in MARL, particularly when deploying it in real-world applications such as autonomous driving, emerges as a critical challenge.
DPN: Decoupling Partition and Navigation for Neural Solvers of Min-max Vehicle Routing Problems
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.
Knowledge Graph Pruning for Recommendation
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.
DynLLM: When Large Language Models Meet Dynamic Graph Recommendation
To bridge this gap, in this paper, we propose a novel framework, called DynLLM, to deal with the dynamic graph recommendation task with LLMs.
Robust Implementation of Retrieval-Augmented Generation on Edge-based Computing-in-Memory Architectures
Large Language Models (LLMs) deployed on edge devices learn through fine-tuning and updating a certain portion of their parameters.
MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies
For data scaling, we introduce a Warmup-Stable-Decay (WSD) learning rate scheduler (LRS), conducive to continuous training and domain adaptation.
Make Large Language Model a Better Ranker
Moreover, these LLM-based methods struggle to effectively address the order relation among candidates, particularly given the scale of ratings.
Harnessing Large Language Models for Text-Rich Sequential Recommendation
Recent advances in Large Language Models (LLMs) have been changing the paradigm of Recommender Systems (RS).
Dynamic Sparse Learning: A Novel Paradigm for Efficient Recommendation
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.
A Cross-View Hierarchical Graph Learning Hypernetwork for Skill Demand-Supply Joint Prediction
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.
MindShift: Leveraging Large Language Models for Mental-States-Based Problematic Smartphone Use Intervention
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.
QASnowball: An Iterative Bootstrapping Framework for High-Quality Question-Answering Data Generation
Recent years have witnessed the success of question answering (QA), especially its potential to be a foundation paradigm for tackling diverse NLP tasks.
Exploring Large Language Model for Graph Data Understanding in Online Job Recommendations
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.
Generative Job Recommendations with Large Language Model
The rapid development of online recruitment services has encouraged the utilization of recommender systems to streamline the job seeking process.
A Survey on Large Language Models for Recommendation
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).
Ranked #1 on
on Amazon Review 2023
Enhancing Chat Language Models by Scaling High-quality Instructional Conversations
Fine-tuning on instruction data has been widely validated as an effective practice for implementing chat language models like ChatGPT.
Drug Package Recommendation via Interaction-aware Graph Induction
Then, the drug interaction graph will be initialized based on medical records and domain knowledge.
BERT-QE: Contextualized Query Expansion for Document Re-ranking
Query expansion aims to mitigate the mismatch between the language used in a query and in a document.
