3 citations · 5 across the 10 of their papers we have counts for
11 papers
Beyond Activation Alignment:The Alignment-Diversity Tradeoff in Task-Aware LLM Quantization
Fei Wang, Chao Xue, Taoran Liu +3
Mixed-precision quantization (MPQ) has become a key technique for deploying large language models under stringent memory and compute constraints. We first identify a phenomenon tha…
Universally Empowering Zeroth-Order Optimization via Adaptive Layer-wise Sampling
Fei Wang, Li Shen, Liang Ding +3
Zeroth-Order optimization presents a promising memory-efficient paradigm for fine-tuning Large Language Models by relying solely on forward passes. However, its practical adoption…
Layer as Puzzle Pieces: Compressing Large Language Models through Layer Concatenation
Fei Wang, Li Shen, Liang Ding +3
Large Language Models excel at natural language processing tasks, but their massive size leads to high computational and storage demands. Recent works have sought to reduce their m…
ChartMaster: Advancing Chart-to-Code Generation with Real-World Charts and Chart Similarity Reinforcement Learning
Wentao Tan, Qiong Cao, Chao Xue +3
The chart-to-code generation task requires MLLMs to convert chart images into executable code. This task faces two main challenges: limited data diversity and the difficulty of mai…
From Answers to Rationales: Self-Aligning Multimodal Reasoning with Answer-Oriented Chain-of-Thought
Wentao Tan, Qiong Cao, Yibing Zhan +2
Achieving human-like reasoning capabilities in Multimodal Large Language Models (MLLMs) has long been a goal. Current methods primarily focus on synthesizing positive rationales, t…
Modeling Thousands of Human Annotators for Generalizable Text-to-Image Person Re-identification
Jiayu Jiang, Changxing Ding, Wentao Tan +3
Text-to-image person re-identification (ReID) aims to retrieve the images of an interested person based on textual descriptions. One main challenge for this task is the high cost i…
Beyond Human Data: Aligning Multimodal Large Language Models by Iterative Self-Evolution
Wentao Tan, Qiong Cao, Yibing Zhan +2
Human preference alignment can greatly enhance Multimodal Large Language Models (MLLMs), but collecting high-quality preference data is costly. A promising solution is the self-evo…
Simultaneous Computation and Memory Efficient Zeroth-Order Optimizer for Fine-Tuning Large Language Models
Fei Wang, Li Shen, Liang Ding +3
Fine-tuning is powerful for adapting large language models to downstream tasks, but it often results in huge memory usages. A promising approach to mitigate this is using Zeroth-Or…
Harnessing the Power of MLLMs for Transferable Text-to-Image Person ReID
Wentao Tan, Changxing Ding, Jiayu Jiang +3
Text-to-image person re-identification (ReID) retrieves pedestrian images according to textual descriptions. Manually annotating textual descriptions is time-consuming, restricting…
Decoupled Prototype Learning for Reliable Test-Time Adaptation
Guowei Wang, Changxing Ding, Wentao Tan +1
Test-time adaptation (TTA) is a task that continually adapts a pre-trained source model to the target domain during inference. One popular approach involves fine-tuning model with…
Unified Pre-training with Pseudo Texts for Text-To-Image Person Re-identification
Zhiyin Shao, Xinyu Zhang, Changxing Ding +2
The pre-training task is indispensable for the text-to-image person re-identification (T2I-ReID) task. However, there are two underlying inconsistencies between these two tasks tha…