activity
20192026
most citedAdaBERT: Task-Adaptive BERT Compression with Differentiable Neural Architecture Search

24 citations · 114 across the 37 of their papers we have counts for

collaborators
Showing 2024Show all

12 papers · 1 filter

cs.CV2024

ArtAug: Enhancing Text-to-Image Generation through Synthesis-Understanding Interaction

Zhongjie Duan, Qianyi Zhao, Cen Chen +4

The emergence of diffusion models has significantly advanced image synthesis. The recent studies of model interaction and self-corrective reasoning approach in large language model…

cs.CV2024★ 1 cited

HumanVBench: Probing Human-Centric Video Understanding in MLLMs with Automatically Synthesized Benchmarks

Ting Zhou, Daoyuan Chen, Qirui Jiao +3

Evaluating the nuanced human-centric video understanding capabilities of Multimodal Large Language Models (MLLMs) remains a great challenge, as existing benchmarks often overlook t…

cs.AI2024★ 1 cited

The Synergy between Data and Multi-Modal Large Language Models: A Survey from Co-Development Perspective

Zhen Qin, Daoyuan Chen, Wenhao Zhang +5

The rapid development of large language models (LLMs) has been witnessed in recent years. Based on the powerful LLMs, multi-modal LLMs (MLLMs) extend the modality from text to a br…

cs.CV2024

Img-Diff: Contrastive Data Synthesis for Multimodal Large Language Models

Qirui Jiao, Daoyuan Chen, Yilun Huang +3

High-performance Multimodal Large Language Models (MLLMs) are heavily dependent on data quality. To advance fine-grained image recognition within MLLMs, we introduce a novel data s…

cs.AI2024

Data-Juicer Sandbox: A Feedback-Driven Suite for Multimodal Data-Model Co-development

Daoyuan Chen, Haibin Wang, Yilun Huang +4

The emergence of multimodal large models has advanced artificial intelligence, introducing unprecedented levels of performance and functionality. However, optimizing these models r…

cs.LG2024

BiMix: A Bivariate Data Mixing Law for Language Model Pretraining

Ce Ge, Zhijian Ma, Daoyuan Chen +2

Large language models have demonstrated remarkable capabilities across various tasks, primarily attributed to the utilization of diversely sourced data. However, the impact of pret…

cs.CL2024★ 2 cited

ChartThinker: A Contextual Chain-of-Thought Approach to Optimized Chart Summarization

Mengsha Liu, Daoyuan Chen, Yaliang Li +2

Data visualization serves as a critical means for presenting data and mining its valuable insights. The task of chart summarization, through natural language processing techniques,…

cs.CL2024

Dynamic Demonstration Retrieval and Cognitive Understanding for Emotional Support Conversation

Zhe Xu, Daoyuan Chen, Jiayi Kuang +3

Emotional Support Conversation (ESC) systems are pivotal in providing empathetic interactions, aiding users through negative emotional states by understanding and addressing their…

cs.MA2024★ 9 cited

AgentScope: A Flexible yet Robust Multi-Agent Platform

Dawei Gao, Zitao Li, Xuchen Pan +16

With the rapid advancement of Large Language Models (LLMs), significant progress has been made in multi-agent applications. However, the complexities in coordinating agents' cooper…

cs.CL2024★ 5 cited

Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resources

Jiamu Bai, Daoyuan Chen, Bingchen Qian +2

Federated Learning (FL) has recently been applied to the parameter-efficient fine-tuning of Large Language Models (LLMs). While promising, it raises significant challenges due to t…

cs.LG2024

On the Convergence of Zeroth-Order Federated Tuning for Large Language Models

Zhenqing Ling, Daoyuan Chen, Liuyi Yao +2

The confluence of Federated Learning (FL) and Large Language Models (LLMs) is ushering in a new era in privacy-preserving natural language processing. However, the intensive memory…

cs.CV2024

From Training-Free to Adaptive: Empirical Insights into MLLMs' Understanding of Detection Information

Qirui Jiao, Daoyuan Chen, Yilun Huang +2

Despite the impressive capabilities of Multimodal Large Language Models (MLLMs) in integrating text and image modalities, challenges remain in accurately interpreting detailed visu…