2 citations · 3 across the 10 of their papers we have counts for
10 papers
NeuroAda: Activating Each Neuron's Potential for Parameter-Efficient Fine-Tuning
Zhi Zhang, Yixian Shen, Congfeng Cao +1
Existing parameter-efficient fine-tuning (PEFT) methods primarily fall into two categories: addition-based and selective in-situ adaptation. The former, such as LoRA, introduce add…
Never compromise with vulnerabilities: a comprehensive survey on AI governance
Yuchu Jiang, Jian Zhao, Yuchen Yuan +62
The rapid advancement of AI has expanded its capabilities across domains, yet introduced critical technical vulnerabilities, such as algorithmic bias and adversarial sensitivity, t…
Lightweight MSA Design Advances Protein Folding From Evolutionary Embeddings
Hanqun Cao, Xinyi Zhou, Zijun Gao +7
Protein structure prediction often hinges on multiple sequence alignments (MSAs), which underperform on low-homology and orphan proteins. We introduce PLAME, a lightweight MSA desi…
Hummus: A Dataset of Humorous Multimodal Metaphor Use
Xiaoyu Tong, Zhi Zhang, Pia Sommerauer +2
Metaphor and humor share a lot of common ground, and metaphor is one of the most common humorous mechanisms. This study focuses on the humorous capacity of multimodal metaphors, wh…
Beyond Words: Exploring Cultural Value Sensitivity in Multimodal Models
Srishti Yadav, Zhi Zhang, Daniel Hershcovich +1
Investigating value alignment in Large Language Models (LLMs) based on cultural context has become a critical area of research. However, similar biases have not been extensively ex…
Proactive Gradient Conflict Mitigation in Multi-Task Learning: A Sparse Training Perspective
Zhi Zhang, Jiayi Shen, Congfeng Cao +5
Advancing towards generalist agents necessitates the concurrent processing of multiple tasks using a unified model, thereby underscoring the growing significance of simultaneous mo…
Cross-modal Information Flow in Multimodal Large Language Models
Zhi Zhang, Srishti Yadav, Fengze Han +1
The recent advancements in auto-regressive multimodal large language models (MLLMs) have demonstrated promising progress for vision-language tasks. While there exists a variety of…
SAN: Hypothesizing Long-Term Synaptic Development and Neural Engram Mechanism in Scalable Model's Parameter-Efficient Fine-Tuning
Gaole Dai, Chun-Kai Fan, Yiming Tang +7
Advances in Parameter-Efficient Fine-Tuning (PEFT) bridged the performance gap with Full Fine-Tuning (FFT) through sophisticated analysis of pre-trained parameter spaces. Starting…
Gradient-based Parameter Selection for Efficient Fine-Tuning
Zhi Zhang, Qizhe Zhang, Zijun Gao +4
With the growing size of pre-trained models, full fine-tuning and storing all the parameters for various downstream tasks is costly and infeasible. In this paper, we propose a new…
CK-Transformer: Commonsense Knowledge Enhanced Transformers for Referring Expression Comprehension
Zhi Zhang, Helen Yannakoudakis, Xiantong Zhen +1
The task of multimodal referring expression comprehension (REC), aiming at localizing an image region described by a natural language expression, has recently received increasing a…