59 citations · 67 across the 10 of their papers we have counts for
10 papers
MaVEn: An Effective Multi-granularity Hybrid Visual Encoding Framework for Multimodal Large Language Model
Chaoya Jiang, Jia Hongrui, Haiyang Xu +6
This paper presents MaVEn, an innovative Multi-granularity Visual Encoding framework designed to enhance the capabilities of Multimodal Large Language Models (MLLMs) in multi-image…
Enhancing In-Context Learning via Implicit Demonstration Augmentation
Xiaoling Zhou, Wei Ye, Yidong Wang +4
The emergence of in-context learning (ICL) enables large pre-trained language models (PLMs) to make predictions for unseen inputs without updating parameters. Despite its potential…
CoderUJB: An Executable and Unified Java Benchmark for Practical Programming Scenarios
Zhengran Zeng, Yidong Wang, Rui Xie +2
In the evolving landscape of large language models (LLMs) tailored for software engineering, the need for benchmarks that accurately reflect real-world development scenarios is par…
Efficient Vision-and-Language Pre-training with Text-Relevant Image Patch Selection
Wei Ye, Chaoya Jiang, Haiyang Xu +6
Vision Transformers (ViTs) have become increasingly popular in large-scale Vision and Language Pre-training (VLP) models. Although previous VLP research has demonstrated the effica…
MusicAgent: An AI Agent for Music Understanding and Generation with Large Language Models
Dingyao Yu, Kaitao Song, Peiling Lu +5
AI-empowered music processing is a diverse field that encompasses dozens of tasks, ranging from generation tasks (e.g., timbre synthesis) to comprehension tasks (e.g., music classi…
Evaluating ChatGPT's Information Extraction Capabilities: An Assessment of Performance, Explainability, Calibration, and Faithfulness
Bo Li, Gexiang Fang, Yang Yang +4
The capability of Large Language Models (LLMs) like ChatGPT to comprehend user intent and provide reasonable responses has made them extremely popular lately. In this paper, we foc…