33 citations · 92 across the 23 of their papers we have counts for
24 papers
QG-CoC: Question-Guided Chain-of-Captions for Large Multimodal Models
Kuei-Chun Kao, Hsu Tzu-Yin, Yunqi Hong +2
Recently, Multimodal Large Language Models (MLLMs) encounter two key issues in multi-image contexts: (1) a lack of fine-grained perception across disparate images, and (2) a dimini…
Concepts or Skills? Rethinking Instruction Selection for Multi-modal Models
Andrew Bai, Justin Cui, Ruochen Wang +1
Vision-language instruction tuning achieves two main purposes: learning visual concepts and learning visual skills. In this paper, we found that vision-language benchmarks fall int…
Don't Think Longer, Think Wisely: Optimizing Thinking Dynamics for Large Reasoning Models
Sohyun An, Ruochen Wang, Tianyi Zhou +1
While recent success of large reasoning models (LRMs) significantly advanced LLMs' reasoning capability by optimizing the final answer accuracy using reinforcement learning, they m…
Exploring Expert Failures Improves LLM Agent Tuning
Li-Cheng Lan, Andrew Bai, Minhao Cheng +2
Large Language Models (LLMs) have shown tremendous potential as agents, excelling at tasks that require multiple rounds of reasoning and interactions. Rejection Sampling Fine-Tunin…
R1-Zero's "Aha Moment" in Visual Reasoning on a 2B Non-SFT Model
Hengguang Zhou, Xirui Li, Ruochen Wang +3
Recently DeepSeek R1 demonstrated how reinforcement learning with simple rule-based incentives can enable autonomous development of complex reasoning in large language models, char…
Mitigating Bias in Dataset Distillation
Justin Cui, Ruochen Wang, Yuanhao Xiong +1
Dataset Distillation has emerged as a technique for compressing large datasets into smaller synthetic counterparts, facilitating downstream training tasks. In this paper, we study…
Solving for X and Beyond: Can Large Language Models Solve Complex Math Problems with More-Than-Two Unknowns?
Kuei-Chun Kao, Ruochen Wang, Cho-Jui Hsieh
Large Language Models (LLMs) have demonstrated remarkable performance in solving math problems, a hallmark of human intelligence. Despite high success rates on current benchmarks;…
One Prompt is not Enough: Automated Construction of a Mixture-of-Expert Prompts
Ruochen Wang, Sohyun An, Minhao Cheng +3
Large Language Models (LLMs) exhibit strong generalization capabilities to novel tasks when prompted with language instructions and in-context demos. Since this ability sensitively…
On Discrete Prompt Optimization for Diffusion Models
Ruochen Wang, Ting Liu, Cho-Jui Hsieh +1
This paper introduces the first gradient-based framework for prompt optimization in text-to-image diffusion models. We formulate prompt engineering as a discrete optimization probl…
Large Language Models are Interpretable Learners
Ruochen Wang, Si Si, Felix Yu +3
The trade-off between expressiveness and interpretability remains a core challenge when building human-centric predictive models for classification and decision-making. While symbo…
MOSSBench: Is Your Multimodal Language Model Oversensitive to Safe Queries?
Xirui Li, Hengguang Zhou, Ruochen Wang +3
Humans are prone to cognitive distortions -- biased thinking patterns that lead to exaggerated responses to specific stimuli, albeit in very different contexts. This paper demonstr…
Understanding the Impact of Negative Prompts: When and How Do They Take Effect?
Yuanhao Ban, Ruochen Wang, Tianyi Zhou +3
The concept of negative prompts, emerging from conditional generation models like Stable Diffusion, allows users to specify what to exclude from the generated images.%, demonstrati…
The Crystal Ball Hypothesis in diffusion models: Anticipating object positions from initial noise
Yuanhao Ban, Ruochen Wang, Tianyi Zhou +3
Diffusion models have achieved remarkable success in text-to-image generation tasks; however, the role of initial noise has been rarely explored. In this study, we identify specifi…
DrAttack: Prompt Decomposition and Reconstruction Makes Powerful LLM Jailbreakers
Xirui Li, Ruochen Wang, Minhao Cheng +2
The safety alignment of Large Language Models (LLMs) is vulnerable to both manual and automated jailbreak attacks, which adversarially trigger LLMs to output harmful content. Howev…
MuLan: Multimodal-LLM Agent for Progressive and Interactive Multi-Object Diffusion
Sen Li, Ruochen Wang, Cho-Jui Hsieh +2
Existing text-to-image models still struggle to generate images of multiple objects, especially in handling their spatial positions, relative sizes, overlapping, and attribute bind…
REVIS: An Error Visualization Tool for Rust
Ruochen Wang, Molly Maclaren, Michael Coblenz
Rust is a programming language that uses a concept of ownership to guarantee memory safety without the use of a garbage collector. However, some error messages related to ownership…
Scaling Up Dataset Distillation to ImageNet-1K with Constant Memory
Justin Cui, Ruochen Wang, Si Si +1
Dataset Distillation is a newly emerging area that aims to distill large datasets into much smaller and highly informative synthetic ones to accelerate training and reduce storage.…
Efficient Non-Parametric Optimizer Search for Diverse Tasks
Ruochen Wang, Yuanhao Xiong, Minhao Cheng +1
Efficient and automated design of optimizers plays a crucial role in full-stack AutoML systems. However, prior methods in optimizer search are often limited by their scalability, g…
FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning
Yuanhao Xiong, Ruochen Wang, Minhao Cheng +2
Federated learning~(FL) has recently attracted increasing attention from academia and industry, with the ultimate goal of achieving collaborative training under privacy and communi…
DC-BENCH: Dataset Condensation Benchmark
Justin Cui, Ruochen Wang, Si Si +1
Dataset Condensation is a newly emerging technique aiming at learning a tiny dataset that captures the rich information encoded in the original dataset. As the size of datasets con…
Generalizing Few-Shot NAS with Gradient Matching
Shoukang Hu, Ruochen Wang, Lanqing Hong +3
Efficient performance estimation of architectures drawn from large search spaces is essential to Neural Architecture Search. One-Shot methods tackle this challenge by training one…
RANK-NOSH: Efficient Predictor-Based Architecture Search via Non-Uniform Successive Halving
Ruochen Wang, Xiangning Chen, Minhao Cheng +2
Predictor-based algorithms have achieved remarkable performance in the Neural Architecture Search (NAS) tasks. However, these methods suffer from high computation costs, as trainin…
Rethinking Architecture Selection in Differentiable NAS
Ruochen Wang, Minhao Cheng, Xiangning Chen +2
Differentiable Neural Architecture Search is one of the most popular Neural Architecture Search (NAS) methods for its search efficiency and simplicity, accomplished by jointly opti…
DrNAS: Dirichlet Neural Architecture Search
Xiangning Chen, Ruochen Wang, Minhao Cheng +2
This paper proposes a novel differentiable architecture search method by formulating it into a distribution learning problem. We treat the continuously relaxed architecture mixing…