2 citations · 3 across the 8 of their papers we have counts for
8 papers
On the Stability of Prompt Ranking in Large Language Model Evaluation
Shaoshuai Du, Penghao Liang, Yixian Shen +3
Prompt-based interaction has become a dominant paradigm for using large language models (LLMs), where multiple candidate prompts are evaluated and the top-ranked one is selected fo…
Performance Review on LLM for solving leetcode problems
Lun Wang, Chuanqi Shi, Shaoshui Du +5
This paper presents a comprehensive performance evaluation of Large Language Models (LLMs) in solving programming challenges from Leetcode, a widely used platform for algorithm pra…
Comparative Analysis of Large Language Models for Context-Aware Code Completion using SAFIM Framework
Hang Zhang, Yanxin Shen, Lun Wang +4
The advent of Large Language Models (LLMs) has revolutionized code completion, transforming it into a more intelligent and context-aware feature in modern integrated development en…
Deep Semantic Graph Learning via LLM based Node Enhancement
Chuanqi Shi, Yiyi Tao, Hang Zhang +4
Graph learning has attracted significant attention due to its widespread real-world applications. Current mainstream approaches rely on text node features and obtain initial node e…
Comparative Analysis of Listwise Reranking with Large Language Models in Limited-Resource Language Contexts
Yanxin Shen, Lun Wang, Chuanqi Shi +4
Large Language Models (LLMs) have demonstrated significant effectiveness across various NLP tasks, including text ranking. This study assesses the performance of large language mod…
AltGen: AI-Driven Alt Text Generation for Enhancing EPUB Accessibility
Yixian Shen, Hang Zhang, Yanxin Shen +4
Digital accessibility is a cornerstone of inclusive content delivery, yet many EPUB files fail to meet fundamental accessibility standards, particularly in providing descriptive al…
Robustness of Large Language Models Against Adversarial Attacks
Yiyi Tao, Yixian Shen, Hang Zhang +4
The increasing deployment of Large Language Models (LLMs) in various applications necessitates a rigorous evaluation of their robustness against adversarial attacks. In this paper,…
NEVLP: Noise-Robust Framework for Efficient Vision-Language Pre-training
Yiyi Tao, Zhuoyue Wang, Hang Zhang +1
The success of Vision Language Models (VLMs) on various vision-language tasks heavily relies on pre-training with large scale web-crawled datasets. However, the noisy and incomplet…