4 citations · 8 across the 16 of their papers we have counts for
5 papers · 1 filter
The First Prompt Counts the Most! An Evaluation of Large Language Models on Iterative Example-Based Code Generation
Yingjie Fu, Bozhou Li, Linyi Li +2
The capabilities of Large Language Models (LLMs) in code generation have been extensively studied, particularly for implementing target functionalities from natural-language descri…
Gradual Learning: Optimizing Fine-Tuning with Partially Mastered Knowledge in Large Language Models
Bozhou Li, Hao Liang, Yang Li +4
During the pretraining phase, large language models (LLMs) acquire vast amounts of knowledge from extensive text corpora. Nevertheless, in later stages such as fine-tuning and infe…
Are Bigger Encoders Always Better in Vision Large Models?
Bozhou Li, Hao Liang, Zimo Meng +1
In recent years, multimodal large language models (MLLMs) have shown strong potential in real-world applications. They are developing rapidly due to their remarkable ability to com…
SynthVLM: Towards High-Quality and Efficient Synthesis of Image-Caption Datasets for Vision-Language Models
Zheng Liu, Hao Liang, Bozhou Li +5
Vision-Language Models (VLMs) have recently emerged, demonstrating remarkable vision-understanding capabilities. However, training these models requires large-scale datasets, which…
A Survey of Multimodal Large Language Model from A Data-centric Perspective
Tianyi Bai, Hao Liang, Binwang Wan +12
Multimodal large language models (MLLMs) enhance the capabilities of standard large language models by integrating and processing data from multiple modalities, including text, vis…