6 papers
WavefrontDiffusion: Dynamic Decoding Schedule for Improved Reasoning
Haojin Yang, Rui Hu, Zequn Sun +3
Diffusion Language Models (DLMs) have shown strong potential for text generation and are becoming a competitive alternative to autoregressive models. The denoising strategy plays a…
EasySteer: A Unified Framework for High-Performance and Extensible LLM Steering
Haolei Xu, Xinyu Mei, Yuchen Yan +5
Large language model (LLM) steering has emerged as a promising paradigm for controlling model behavior at inference time through targeted manipulation of hidden states, offering a…
Explore-on-Graph: Incentivizing Autonomous Exploration of Large Language Models on Knowledge Graphs with Path-refined Reward Modeling
Shiqi Yan, Yubo Chen, Ruiqi Zhou +8
The reasoning process of Large Language Models (LLMs) is often plagued by hallucinations and missing facts in question-answering tasks. A promising solution is to ground LLMs' answ…
The Harder The Better: Maintaining Supervised Fine-tuning Generalization with Less but Harder Data
Zhaoyang Shang, Sibo Wei, Jianbin Guo +3
Large Language Models (LLMs) excel in general tasks, but adapting them to specialized domains relies on high-quality supervised fine-tuning (SFT) data. Although existing methods ca…
Parametric-ControlNet: Multimodal Control in Foundation Models for Precise Engineering Design Synthesis
Rui Zhou, Yanxia Zhang, Chenyang Yuan +4
This paper introduces a generative model designed for multimodal control over text-to-image foundation generative AI models such as Stable Diffusion, specifically tailored for engi…
Bridging Design Gaps: A Parametric Data Completion Approach With Graph Guided Diffusion Models
Rui Zhou, Chenyang Yuan, Frank Permenter +4
This study introduces a generative imputation model leveraging graph attention networks and tabular diffusion models for completing missing parametric data in engineering designs.…