68 citations · 102 across the 22 of their papers we have counts for
12 papers · 1 filter
ADEM-VL: Adaptive and Embedded Fusion for Efficient Vision-Language Tuning
Zhiwei Hao, Jianyuan Guo, Li Shen +3
Recent advancements in multimodal fusion have witnessed the remarkable success of vision-language (VL) models, which excel in various multimodal applications such as image captioni…
: Improving Code Generation of LLMs by Uncertainty-Aware Selective Contrastive Decoding
Shuai Wang, Liang Ding, Li Shen +4
Large language models (LLMs) have shown remarkable capabilities in code generation. However, the effects of hallucinations (e.g., output noise) make it particularly challenging for…
SMILE: Zero-Shot Sparse Mixture of Low-Rank Experts Construction From Pre-Trained Foundation Models
Anke Tang, Li Shen, Yong Luo +5
Deep model training on extensive datasets is increasingly becoming cost-prohibitive, prompting the widespread adoption of deep model fusion techniques to leverage knowledge from pr…
Towards Efficient Pareto Set Approximation via Mixture of Experts Based Model Fusion
Anke Tang, Li Shen, Yong Luo +4
Solving multi-objective optimization problems for large deep neural networks is a challenging task due to the complexity of the loss landscape and the expensive computational cost…
FusionBench: A Unified Library and Comprehensive Benchmark for Deep Model Fusion
Anke Tang, Li Shen, Yong Luo +5
Deep model fusion is an emerging technique that unifies the predictions or parameters of several deep neural networks into a single better-performing model in a cost-effective and…
Federated Learning with Only Positive Labels by Exploring Label Correlations
Xuming An, Dui Wang, Li Shen +5
Federated learning aims to collaboratively learn a model by using the data from multiple users under privacy constraints. In this paper, we study the multi-label classification pro…
Building Accurate Translation-Tailored LLMs with Language Aware Instruction Tuning
Changtong Zan, Liang Ding, Li Shen +3
Translation-tailored Large language models (LLMs) exhibit remarkable translation capabilities, even competing with supervised-trained commercial translation systems. However, off-t…
OOP: Object-Oriented Programming Evaluation Benchmark for Large Language Models
Shuai Wang, Liang Ding, Li Shen +3
Advancing automated programming necessitates robust and comprehensive code generation benchmarks, yet current evaluation frameworks largely neglect object-oriented programming (OOP…
WisdoM: Improving Multimodal Sentiment Analysis by Fusing Contextual World Knowledge
Wenbin Wang, Liang Ding, Li Shen +3
Sentiment analysis is rapidly advancing by utilizing various data modalities (e.g., text, image). However, most previous works relied on superficial information, neglecting the inc…
Revisiting Knowledge Distillation for Autoregressive Language Models
Qihuang Zhong, Liang Ding, Li Shen +3
Knowledge distillation (KD) is a common approach to compress a teacher model to reduce its inference cost and memory footprint, by training a smaller student model. However, in the…
Merging Multi-Task Models via Weight-Ensembling Mixture of Experts
Anke Tang, Li Shen, Yong Luo +3
Merging various task-specific Transformer-based models trained on different tasks into a single unified model can execute all the tasks concurrently. Previous methods, exemplified…
FreeStyle: Free Lunch for Text-guided Style Transfer using Diffusion Models
Feihong He, Gang Li, Fuhui Sun +4
The rapid development of generative diffusion models has significantly advanced the field of style transfer. However, most current style transfer methods based on diffusion models…