105 citations · 116 across the 9 of their papers we have counts for
10 papers
RankLLM: Weighted Ranking of LLMs by Quantifying Question Difficulty
Ziqian Zhang, Xingjian Hu, Yue Huang +8
Benchmarks establish a standardized evaluation framework to systematically assess the performance of large language models (LLMs), facilitating objective comparisons and driving ad…
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…
NodeRAG: Structuring Graph-based RAG with Heterogeneous Nodes
Tianyang Xu, Haojie Zheng, Chengze Li +4
Retrieval-augmented generation (RAG) empowers large language models to access external and private corpus, enabling factually consistent responses in specific domains. By exploitin…
On the Trustworthiness of Generative Foundation Models: Guideline, Assessment, and Perspective
Yue Huang, Chujie Gao, Siyuan Wu +63
Generative Foundation Models (GenFMs) have emerged as transformative tools. However, their widespread adoption raises critical concerns regarding trustworthiness across dimensions.…
Thinking Before Looking: Improving Multimodal LLM Reasoning via Mitigating Visual Hallucination
Haojie Zheng, Tianyang Xu, Hanchi Sun +3
Multimodal large language models (MLLMs) have advanced the integration of visual and linguistic modalities, establishing themselves as the dominant paradigm for visual-language tas…
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…
Rethinking and Red-Teaming Protective Perturbation in Personalized Diffusion Models
Yixin Liu, Ruoxi Chen, Xun Chen +1
Personalized diffusion models (PDMs) have become prominent for adapting pre-trained text-to-image models to generate images of specific subjects using minimal training data. Howeve…
Mora: Enabling Generalist Video Generation via A Multi-Agent Framework
Zhengqing Yuan, Yixin Liu, Yihan Cao +10
Text-to-video generation has made significant strides, but replicating the capabilities of advanced systems like OpenAI Sora remains challenging due to their closed-source nature.…
Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models
Yixin Liu, Kai Zhang, Yuan Li +9
Sora is a text-to-video generative AI model, released by OpenAI in February 2024. The model is trained to generate videos of realistic or imaginative scenes from text instructions…
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…