5 citations · 6 across the 10 of their papers we have counts for
12 papers
Rethinking Soft Tokens for Parallel Decoding in Diffusion Language Models
Kodai Kawamura, Kenji Kawaguchi, Anji Liu
Diffusion language models (DLMs) enable parallel generation by predicting and committing multiple tokens at each denoising step, yet they can generate individually plausible but mu…
Speculative Correction: Draft-then-Refine Decoding for Diffusion Language Models
Brian K Chen, Chong Wu, Kenji Kawaguchi
Diffusion language models (DLMs) can revise tokens bidirectionally, but standard decoding procedures often adapt them to left-to-right generation by producing text block by block.…
SeedPrints: Fingerprints Can Even Tell Which Seed Your Large Language Model Was Trained From
Yao Tong, Haonan Wang, Siquan Li +2
Fingerprinting Large Language Models (LLMs)is essential for provenance verification and model attribution. Existing fingerprinting methods are primarily evaluated after fine-tuning…
Any-Order GPT as Masked Diffusion Model: Decoupling Formulation and Architecture
Shuchen Xue, Tianyu Xie, Tianyang Hu +5
Efficiently scaling Large Language Models (LLMs) necessitates exploring alternatives to dominant autoregressive (AR) methods, with Masked Diffusion Models (MDMs) emerging as candid…
PrefixMemory-Tuning: Modernizing Prefix-Tuning by Decoupling the Prefix from Attention
Haonan Wang, Brian Chen, Siquan Li +4
Parameter-Efficient Fine-Tuning (PEFT) methods have become crucial for rapidly adapting large language models (LLMs) to downstream tasks. Prefix-Tuning, an early and effective PEFT…
Fostering Video Reasoning via Next-Event Prediction
Haonan Wang, Hongfu Liu, Xiangyan Liu +4
Next-token prediction serves as the foundational learning task enabling reasoning in LLMs. But what should the learning task be when aiming to equip MLLMs with temporal reasoning c…
Memory-Efficient LLM Training by Various-Grained Low-Rank Projection of Gradients
Yezhen Wang, Zhouhao Yang, Brian K Chen +4
Building upon the success of low-rank adapter (LoRA), low-rank gradient projection (LoRP) has emerged as a promising solution for memory-efficient fine-tuning. However, existing Lo…
Reward-Instruct: A Reward-Centric Approach to Fast Photo-Realistic Image Generation
Yihong Luo, Tianyang Hu, Weijian Luo +2
This paper addresses the challenge of achieving high-quality and fast image generation that aligns with complex human preferences. While recent advancements in diffusion models and…
Exact Conversion of In-Context Learning to Model Weights in Linearized-Attention Transformers
Brian K Chen, Tianyang Hu, Hui Jin +2
In-Context Learning (ICL) has been a powerful emergent property of large language models that has attracted increasing attention in recent years. In contrast to regular gradient-ba…
Referee Can Play: An Alternative Approach to Conditional Generation via Model Inversion
Xuantong Liu, Tianyang Hu, Wenjia Wang +2
As a dominant force in text-to-image generation tasks, Diffusion Probabilistic Models (DPMs) face a critical challenge in controllability, struggling to adhere strictly to complex,…
The Surprising Effectiveness of Skip-Tuning in Diffusion Sampling
Jiajun Ma, Shuchen Xue, Tianyang Hu +5
With the incorporation of the UNet architecture, diffusion probabilistic models have become a dominant force in image generation tasks. One key design in UNet is the skip connectio…
Getting More Juice Out of Your Data: Hard Pair Refinement Enhances Visual-Language Models Without Extra Data
Haonan Wang, Minbin Huang, Runhui Huang +7
Contrastive Language-Image Pre-training (CLIP) has become the standard for cross-modal image-text representation learning. Improving CLIP typically requires additional data and ret…