no code implementations • 10 Apr 2024 • Yongqiang Ma, Lizhi Qing, Jiawei Liu, Yangyang Kang, Yue Zhang, Wei Lu, Xiaozhong Liu, Qikai Cheng
Therefore, our study shifts the focus from model-centered to human-centered evaluation in the context of AI-powered writing assistance applications.
2 code implementations • 20 Mar 2024 • Yaowei Zheng, Richong Zhang, Junhao Zhang, Yanhan Ye, Zheyan Luo, Yongqiang Ma
Efficient fine-tuning is vital for adapting large language models (LLMs) to downstream tasks.
no code implementations • 14 Mar 2024 • Hao Zhang, Wenqi Shao, Hong Liu, Yongqiang Ma, Ping Luo, Yu Qiao, Kaipeng Zhang
To bridge this gap, we introduce AVIBench, a framework designed to analyze the robustness of LVLMs when facing various adversarial visual-instructions (AVIs), including four types of image-based AVIs, ten types of text-based AVIs, and nine types of content bias AVIs (such as gender, violence, cultural, and racial biases, among others).
no code implementations • 11 Mar 2024 • Yulong Liu, Yongqiang Ma, Guibo Zhu, Haodong Jing, Nanning Zheng
Our model integrates a high-level perception decoding pipeline and a pixel-wise reconstruction pipeline guided by high-level perceptions, simulating bottom-up and top-down processes in neuroscience.
no code implementations • 28 Feb 2024 • Yulong Liu, Yunlong Yuan, Chunwei Wang, Jianhua Han, Yongqiang Ma, Li Zhang, Nanning Zheng, Hang Xu
In this work, we introduce a novel tool invocation pipeline designed to control massive real-world APIs.
no code implementations • 5 May 2023 • Jiawei Liu, Zi Xiong, Yi Jiang, Yongqiang Ma, Wei Lu, Yong Huang, Qikai Cheng
Inspired by recent advancement in prompt learning, in this paper, we propose the Mix Prompt Tuning (MPT), which is a semi-supervised method to alleviate the dependence on annotated data and improve the performance of multi-granularity academic function recognition tasks with a small number of labeled examples.
1 code implementation • 25 Feb 2023 • Yulong Liu, Yongqiang Ma, Wei Zhou, Guibo Zhu, Nanning Zheng
Our experiments show that this combination can boost the decoding model's performance on certain tasks like fMRI-text matching and fMRI-to-image generation.
no code implementations • 24 Jan 2023 • Yongqiang Ma, Jiawei Liu, Fan Yi, Qikai Cheng, Yong Huang, Wei Lu, Xiaozhong Liu
We find that there exists a "writing style" gap between AI-generated scientific text and human-written scientific text.
no code implementations • 29 Sep 2021 • Kai Chen, Yongqiang Ma, Mingyang Sheng, Nanning Zheng
Inspired by the mechanism of human visual attention, in this paper, we propose a novel method of reconstructing visual stimulus images, which first decodes the distribution of visual attention from fMRI, and then reconstructs the visual images guided by visual attention.
no code implementations • 1 Dec 2017 • Siyu Yu, Nanning Zheng, Yongqiang Ma, Hao Wu, Badong Chen
Analyzing the correlations of collected data from human brain activities and representing activity patterns are two problems in brain decoding based on functional magnetic resonance imaging (fMRI) signals.