activity
20242026
collaborators

7 papers

cs.CV2026

SRA 2: Variational Autoencoder Self-Representation Alignment for Efficient Diffusion Training

Mengmeng Wang, Dengyang Jiang, Liuzhuozheng Li +6

Denoising-based diffusion transformers, despite their strong generation performance, suffer from inefficient training convergence. Existing methods addressing this issue, such as R…

cs.CL2025

A Good Plan is Hard to Find: Aligning Models with Preferences is Misaligned with What Helps Users

Nishant Balepur, Matthew Shu, Yoo Yeon Sung +5

To assist users in complex tasks, LLMs generate plans: step-by-step instructions towards a goal. While alignment methods aim to ensure LLM plans are helpful, they train (RLHF) or e…

cs.CV2025

RobuQ: Pushing DiTs to W1.58A2 via Robust Activation Quantization

Kaicheng Yang, Xun Zhang, Haotong Qin +4

Diffusion Transformers (DiTs) have recently emerged as a powerful backbone for image generation, demonstrating superior scalability and performance over U-Net architectures. Howeve…

cs.CV2025

Attention Debiasing for Token Pruning in Vision Language Models

Kai Zhao, Wubang Yuan, Yuchen Lin +5

Vision-language models (VLMs) typically encode substantially more visual tokens than text tokens, resulting in significant token redundancy. Pruning uninformative visual tokens is…

cs.CL2025

Information-Guided Identification of Training Data Imprint in (Proprietary) Large Language Models

Abhilasha Ravichander, Jillian Fisher, Taylor Sorensen +6

High-quality training data has proven crucial for developing performant large language models (LLMs). However, commercial LLM providers disclose few, if any, details about the data…

cs.CL2024

Augmenting emotion features in irony detection with Large language modeling

Yucheng Lin, Yuhan Xia, Yunfei Long

This study introduces a novel method for irony detection, applying Large Language Models (LLMs) with prompt-based learning to facilitate emotion-centric text augmentation. Traditio…

cs.AI2024

L3GO: Language Agents with Chain-of-3D-Thoughts for Generating Unconventional Objects

Yutaro Yamada, Khyathi Chandu, Yuchen Lin +3

Diffusion-based image generation models such as DALL-E 3 and Stable Diffusion-XL demonstrate remarkable capabilities in generating images with realistic and unique compositions. Ye…