35 citations · 47 across the 15 of their papers we have counts for
16 papers
VulReaD: Knowledge-Graph-guided Software Vulnerability Reasoning and Detection
Samal Mukhtar, Yinghua Yao, Zhu Sun +3
Software vulnerability detection (SVD) is a critical challenge in modern systems. Large language models (LLMs) offer natural-language explanations alongside predictions, but most w…
ERNIE 5.0 Technical Report
Haifeng Wang, Hua Wu, Tian Wu +432
In this report, we introduce ERNIE 5.0, a natively autoregressive foundation model desinged for unified multimodal understanding and generation across text, image, video, and audio…
A Comparative Study of Traditional Machine Learning, Deep Learning, and Large Language Models for Mental Health Forecasting using Smartphone Sensing Data
Kaidong Feng, Zhu Sun, Roy Ka-Wei Lee +3
Smartphone sensing offers an unobtrusive and scalable way to track daily behaviors linked to mental health, capturing changes in sleep, mobility, and phone use that often precede s…
iTIMO: An LLM-empowered Synthesis Dataset for Travel Itinerary Modification
Zhuoxuan Huang, Yunshan Ma, Hongyu Zhang +2
Addressing itinerary modification is crucial for enhancing the travel experience as it is a frequent requirement during traveling. However, existing research mainly focuses on fixe…
Scoring, Reasoning, and Selecting the Best! Ensembling Large Language Models via a Peer-Review Process
Zhijun Chen, Zeyu Ji, Qianren Mao +13
We propose LLM-PeerReview, an unsupervised LLM Ensemble method that selects the most ideal response from multiple LLM-generated candidates for each query, harnessing the collective…
HyMoERec: Hybrid Mixture-of-Experts for Sequential Recommendation
Kunrong Li, Zhu Sun, Kwan Hui Lim
We propose HyMoERec, a novel sequential recommendation framework that addresses the limitations of uniform Position-wise Feed-Forward Networks in existing models. Current approache…
Mitigating Strategy-Selection Bias in Reasoning for More Effective Test-Time Scaling
Zongqian Wu, Baoduo Xu, Tianyu Li +3
Test-time scaling (TTS) has been shown to improve the performance of large language models (LLMs) by sampling and aggregating diverse reasoning paths. However, existing research ha…
Research on Conversational Recommender System Considering Consumer Types
Yaying Luo, Hui Fang, Zhu Sun
Conversational Recommender Systems (CRS) provide personalized services through multi-turn interactions, yet most existing methods overlook users' heterogeneous decision-making styl…
Routing Distilled Knowledge via Mixture of LoRA Experts for Large Language Model based Bundle Generation
Kaidong Feng, Zhu Sun, Hui Fang +3
Large Language Models (LLMs) have shown potential in automatic bundle generation but suffer from prohibitive computational costs. Although knowledge distillation offers a pathway t…
Does Knowledge Distillation Matter for Large Language Model based Bundle Generation?
Kaidong Feng, Zhu Sun, Jie Yang +3
LLMs are increasingly explored for bundle generation, thanks to their reasoning capabilities and knowledge. However, deploying large-scale LLMs introduces significant efficiency ch…
A Reinforcement Learning Method to Factual and Counterfactual Explanations for Session-based Recommendation
Han Zhou, Hui Fang, Zhu Sun +1
Session-based Recommendation (SR) systems have recently achieved considerable success, yet their complex, "black box" nature often obscures why certain recommendations are made. Ex…
Mitigating Visual Forgetting via Take-along Visual Conditioning for Multi-modal Long CoT Reasoning
Hai-Long Sun, Zhun Sun, Houwen Peng +1
Recent advancements in Large Language Models (LLMs) have demonstrated enhanced reasoning capabilities, evolving from Chain-of-Thought (CoT) prompting to advanced, product-oriented…
Towards Fair and Rigorous Evaluations: Hyperparameter Optimization for Top-N Recommendation Task with Implicit Feedback
Hui Fang, Xu Feng, Lu Qin +1
The widespread use of the internet has led to an overwhelming amount of data, which has resulted in the problem of information overload. Recommender systems have emerged as a solut…
A Simple Yet Effective Approach for Diversified Session-Based Recommendation
Qing Yin, Hui Fang, Zhu Sun +1
Session-based recommender systems (SBRSs) have become extremely popular in view of the core capability of capturing short-term and dynamic user preferences. However, most SBRSs pri…
Large Language Models for Intent-Driven Session Recommendations
Zhu Sun, Hongyang Liu, Xinghua Qu +3
Intent-aware session recommendation (ISR) is pivotal in discerning user intents within sessions for precise predictions. Traditional approaches, however, face limitations due to th…
Adaptive In-Context Learning with Large Language Models for Bundle Generation
Zhu Sun, Kaidong Feng, Jie Yang +4
Most existing bundle generation approaches fall short in generating fixed-size bundles. Furthermore, they often neglect the underlying user intents reflected by the bundles in the…