5 citations · 17 across the 13 of their papers we have counts for
13 papers
MultiRef: Controllable Image Generation with Multiple Visual References
Ruoxi Chen, Dongping Chen, Siyuan Wu +6
Visual designers naturally draw inspiration from multiple visual references, combining diverse elements and aesthetic principles to create artwork. However, current image generativ…
CrowdSelect: Synthetic Instruction Data Selection with Multi-LLM Wisdom
Yisen Li, Lingfeng Yang, Wenxuan Shen +4
Distilling advanced Large Language Models' instruction-following capabilities into smaller models using a selected subset has become a mainstream approach in model training. While…
Interleaved Scene Graphs for Interleaved Text-and-Image Generation Assessment
Dongping Chen, Ruoxi Chen, Shu Pu +8
Many real-world user queries (e.g. "How do to make egg fried rice?") could benefit from systems capable of generating responses with both textual steps with accompanying images, si…
The Impact of Large Language Models in Academia: from Writing to Speaking
Mingmeng Geng, Caixi Chen, Yanru Wu +3
Large language models (LLMs) are increasingly impacting human society, particularly in textual information. Based on more than 30,000 papers and 1,000 presentations from machine le…
Self-Cognition in Large Language Models: An Exploratory Study
Dongping Chen, Jiawen Shi, Yao Wan +3
While Large Language Models (LLMs) have achieved remarkable success across various applications, they also raise concerns regarding self-cognition. In this paper, we perform a pion…
DataGen: Unified Synthetic Dataset Generation via Large Language Models
Yue Huang, Siyuan Wu, Chujie Gao +8
Large Language Models (LLMs) such as GPT-4 and Llama3 have significantly impacted various fields by enabling high-quality synthetic data generation and reducing dependence on expen…
Jailbreaking Large Language Models Through Alignment Vulnerabilities in Out-of-Distribution Settings
Yue Huang, Jingyu Tang, Dongping Chen +5
Recently, Large Language Models (LLMs) have garnered significant attention for their exceptional natural language processing capabilities. However, concerns about their trustworthi…
GUI-World: A Video Benchmark and Dataset for Multimodal GUI-oriented Understanding
Dongping Chen, Yue Huang, Siyuan Wu +17
Recently, Multimodal Large Language Models (MLLMs) have been used as agents to control keyboard and mouse inputs by directly perceiving the Graphical User Interface (GUI) and gener…
HonestLLM: Toward an Honest and Helpful Large Language Model
Chujie Gao, Siyuan Wu, Yue Huang +6
Large Language Models (LLMs) have achieved remarkable success across various industries due to their exceptional generative capabilities. However, for safe and effective real-world…
I Think, Therefore I am: Benchmarking Awareness of Large Language Models Using AwareBench
Yuan Li, Yue Huang, Yuli Lin +3
Do large language models (LLMs) exhibit any forms of awareness similar to humans? In this paper, we introduce AwareBench, a benchmark designed to evaluate awareness in LLMs. Drawin…
MLLM-as-a-Judge: Assessing Multimodal LLM-as-a-Judge with Vision-Language Benchmark
Dongping Chen, Ruoxi Chen, Shilin Zhang +7
Multimodal Large Language Models (MLLMs) have gained significant attention recently, showing remarkable potential in artificial general intelligence. However, assessing the utility…
LLM-as-a-Coauthor: Can Mixed Human-Written and Machine-Generated Text Be Detected?
Qihui Zhang, Chujie Gao, Dongping Chen +8
With the rapid development and widespread application of Large Language Models (LLMs), the use of Machine-Generated Text (MGT) has become increasingly common, bringing with it pote…
MetaTool Benchmark for Large Language Models: Deciding Whether to Use Tools and Which to Use
Yue Huang, Jiawen Shi, Yuan Li +8
Large language models (LLMs) have garnered significant attention due to their impressive natural language processing (NLP) capabilities. Recently, many studies have focused on the…