2 citations · 3 across the 9 of their papers we have counts for
9 papers
S-SPPO: Semantic-Calibrated Self-Play Preference Optimization
Xiwen Chen, Wenhui Zhu, Jingjing Wang +13
Aligning Large Language Models (LLMs) with human preferences is often formulated via Direct Preference Optimization (DPO). However, the standard Bradley-Terry instantiation of DPO…
Context Pruning for Coding Agents via Multi-Rubric Latent Reasoning
Jingjing Wang, Xiwen Chen, Wenhui Zhu +6
LLM-powered coding agents spend the majority of their token budget reading repository files, yet much of the retrieved code is irrelevant to the task at hand. Existing learned prun…
SODA: Semi On-Policy Black-Box Distillation for Large Language Models
Xiwen Chen, Jingjing Wang, Wenhui Zhu +7
Black-box knowledge distillation for large language models presents a strict trade-off. Simple off-policy methods (e.g., sequence-level knowledge distillation) struggle to correct…
Climate Prompting: Generating the Madden-Julian Oscillation using Video Diffusion and Low-Dimensional Conditioning
Sulian Thual, Feiyang Cai, Jingjing Wang +1
Generative Deep Learning is a powerful tool for modeling of the Madden-Julian oscillation (MJO) in the tropics, yet its relationship to traditional theoretical frameworks remains p…
MolLangData: A Large-Scale Dataset for Molecular Structure-Language Description via a Rule-Regularized Method
Feiyang Cai, Guijuan He, Yi Hu +7
Molecular function is largely determined by structure. Accurately aligning molecular structure with natural language is therefore essential for enabling large language models (LLMs…
Integration Flow Models
Jingjing Wang, Dan Zhang, Joshua Luo +2
Ordinary differential equation (ODE) based generative models have emerged as a powerful approach for producing high-quality samples in many applications. However, the ODE-based met…
Unified Directly Denoising for Both Variance Preserving and Variance Exploding Diffusion Models
Jingjing Wang, Dan Zhang, Feng Luo
Previous work has demonstrated that, in the Variance Preserving (VP) scenario, the nascent Directly Denoising Diffusion Models (DDDM) can generate high-quality images in one step w…
Directly Denoising Diffusion Models
Dan Zhang, Jingjing Wang, Feng Luo
In this paper, we present the Directly Denoising Diffusion Model (DDDM): a simple and generic approach for generating realistic images with few-step sampling, while multistep sampl…
Is GPT Powerful Enough to Analyze the Emotions of Memes?
Jingjing Wang, Joshua Luo, Grace Yang +2
Large Language Models (LLMs), representing a significant achievement in artificial intelligence (AI) research, have demonstrated their ability in a multitude of tasks. This project…