14 citations · 17 across the 15 of their papers we have counts for
16 papers
Few-Step Diffusion Language Models via Trajectory Self-Distillation
Tunyu Zhang, Xinxi Zhang, Ligong Han +9
Diffusion large language models (DLLMs) have emerged as powerful generative models with the promise of fast text generation through parallel decoding. However, realizing this poten…
Overcoming the Curvature Bottleneck in MeanFlow
Xinxi Zhang, Shiwei Tan, Quang Nguyen +7
MeanFlow offers a promising framework for one-step generative modeling by directly learning a mean-velocity field, bypassing expensive numerical integration. However, we find that…
M^3-Bench: Multi-Modal, Multi-Hop, Multi-Threaded Tool-Using MLLM Agent Benchmark
Yang Zhou, Mingyu Zhao, Zhenting Wang +6
We present M^3-Bench, the first benchmark for evaluating multimodal tool use under the Model Context Protocol. The benchmark targets realistic, multi-hop and multi-threaded workflo…
Discrete Noise Inversion for Next-scale Autoregressive Text-based Image Editing
Quan Dao, Xiaoxiao He, Ligong Han +6
Visual autoregressive models (VAR) have recently emerged as a promising class of generative models, achieving performance comparable to diffusion models in text-to-image generation…
AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
Can Jin, Yang Zhou, Qixin Zhang +8
Test-time scaling strategies for Large Language Models predominantly rely on either reinforcement learning with sparse outcome rewards or search-based methods guided by static Proc…
TokUR: Token-Level Uncertainty Estimation for Large Language Model Reasoning
Tunyu Zhang, Haizhou Shi, Yibin Wang +9
While Large Language Models (LLMs) have demonstrated impressive capabilities, their output quality remains inconsistent across various application scenarios, making it difficult to…
Can Large Vision-Language Models Detect Images Copyright Infringement from GenAI?
Qipan Xu, Zhenting Wang, Xiaoxiao He +2
Generative AI models, renowned for their ability to synthesize high-quality content, have sparked growing concerns over the improper generation of copyright-protected material. Whi…
LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation
Can Jin, Ying Li, Mingyu Zhao +6
Visual prompting has gained popularity as a method for adapting pre-trained models to specific tasks, particularly in the realm of parameter-efficient tuning. However, existing vis…
Rate-My-LoRA: Efficient and Adaptive Federated Model Tuning for Cardiac MRI Segmentation
Xiaoxiao He, Haizhou Shi, Ligong Han +7
Cardiovascular disease (CVD) and cardiac dyssynchrony are major public health problems in the United States. Precise cardiac image segmentation is crucial for extracting quantitati…
MLLM-as-a-Judge for Image Safety without Human Labeling
Zhenting Wang, Shuming Hu, Shiyu Zhao +12
Image content safety has become a significant challenge with the rise of visual media on online platforms. Meanwhile, in the age of AI-generated content (AIGC), many image generati…
Training-Free Bayesianization for Low-Rank Adapters of Large Language Models
Haizhou Shi, Yibin Wang, Ligong Han +2
Estimating the uncertainty of responses from Large Language Models (LLMs) remains a critical challenge. While recent Bayesian methods have demonstrated effectiveness in quantifying…
DICE: Discrete Inversion Enabling Controllable Editing for Multinomial Diffusion and Masked Generative Models
Xiaoxiao He, Quan Dao, Ligong Han +14
Discrete diffusion models have achieved success in tasks like image generation and masked language modeling but face limitations in controlled content editing. We introduce DICE (D…
APEER: Automatic Prompt Engineering Enhances Large Language Model Reranking
Can Jin, Hongwu Peng, Shiyu Zhao +7
Large Language Models (LLMs) have significantly enhanced Information Retrieval (IR) across various modules, such as reranking. Despite impressive performance, current zero-shot rel…
BLoB: Bayesian Low-Rank Adaptation by Backpropagation for Large Language Models
Yibin Wang, Haizhou Shi, Ligong Han +2
Large Language Models (LLMs) often suffer from overconfidence during inference, particularly when adapted to downstream domain-specific tasks with limited data. Previous work addre…
Spectrum-Aware Parameter Efficient Fine-Tuning for Diffusion Models
Xinxi Zhang, Song Wen, Ligong Han +6
Adapting large-scale pre-trained generative models in a parameter-efficient manner is gaining traction. Traditional methods like low rank adaptation achieve parameter efficiency by…
Implicit In-context Learning
Zhuowei Li, Zihao Xu, Ligong Han +5
In-context Learning (ICL) empowers large language models (LLMs) to swiftly adapt to unseen tasks at inference-time by prefixing a few demonstration examples before queries. Despite…