5 citations · 9 across the 13 of their papers we have counts for
14 papers
Persistent Visual Memory: Sustaining Perception for Deep Generation in LVLMs
Siyuan Huang, Xiaoye Qu, Yafu Li +6
While autoregressive Large Vision-Language Models (LVLMs) demonstrate remarkable proficiency in multimodal tasks, they face a "Visual Signal Dilution" phenomenon, where the accumul…
Scaling Reasoning, Losing Control: Evaluating Instruction Following in Large Reasoning Models
Tingchen Fu, Jiawei Gu, Yafu Li +2
Instruction-following is essential for aligning large language models (LLMs) with user intent. While recent reasoning-oriented models exhibit impressive performance on complex math…
CoCA: Step-level Reward for Free in RL-based T2I Diffusion Model Fine-tuning
Xinyao Liao, Wei Wei, Xiaoye Qu +3
Recent advances in text-to-image (T2I) diffusion model fine-tuning leverage reinforcement learning (RL) to align generated images with learnable reward functions. The existing appr…
SATORI-R1: Incentivizing Multimodal Reasoning through Explicit Visual Anchoring
Chuming Shen, Wei Wei, Xiaoye Qu +1
DeepSeek-R1 has demonstrated powerful reasoning capabilities in the text domain through stable reinforcement learning (RL). Recently, in the multimodal domain, works have begun to…
Extrapolating and Decoupling Image-to-Video Generation Models: Motion Modeling is Easier Than You Think
Jie Tian, Xiaoye Qu, Zhenyi Lu +3
Image-to-Video (I2V) generation aims to synthesize a video clip according to a given image and condition (e.g., text). The key challenge of this task lies in simultaneously generat…
Make LoRA Great Again: Boosting LoRA with Adaptive Singular Values and Mixture-of-Experts Optimization Alignment
Chenghao Fan, Zhenyi Lu, Sichen Liu +4
While Low-Rank Adaptation (LoRA) enables parameter-efficient fine-tuning for Large Language Models (LLMs), its performance often falls short of Full Fine-Tuning (Full FT). Current…
Look, Compare, Decide: Alleviating Hallucination in Large Vision-Language Models via Multi-View Multi-Path Reasoning
Xiaoye Qu, Jiashuo Sun, Wei Wei +1
Recently, Large Vision-Language Models (LVLMs) have demonstrated impressive capabilities in multi-modal context comprehension. However, they still suffer from hallucination problem…
Mitigating Multilingual Hallucination in Large Vision-Language Models
Xiaoye Qu, Mingyang Song, Wei Wei +2
While Large Vision-Language Models (LVLMs) have exhibited remarkable capabilities across a wide range of tasks, they suffer from hallucination problems, where models generate plaus…
Mitigating Boundary Ambiguity and Inherent Bias for Text Classification in the Era of Large Language Models
Zhenyi Lu, Jie Tian, Wei Wei +4
Text classification is a crucial task encountered frequently in practical scenarios, yet it is still under-explored in the era of large language models (LLMs). This study shows tha…
On Giant's Shoulders: Effortless Weak to Strong by Dynamic Logits Fusion
Chenghao Fan, Zhenyi Lu, Wei Wei +4
Efficient fine-tuning of large language models for task-specific applications is imperative, yet the vast number of parameters in these models makes their training increasingly cha…
Twin-Merging: Dynamic Integration of Modular Expertise in Model Merging
Zhenyi Lu, Chenghao Fan, Wei Wei +3
In the era of large language models, model merging is a promising way to combine multiple task-specific models into a single multitask model without extra training. However, two ch…
Reinforcement Learning with Token-level Feedback for Controllable Text Generation
Wendi Li, Wei Wei, Kaihe Xu +3
To meet the requirements of real-world applications, it is essential to control generations of large language models (LLMs). Prior research has tried to introduce reinforcement lea…
Learning the Unlearned: Mitigating Feature Suppression in Contrastive Learning
Jihai Zhang, Xiang Lan, Xiaoye Qu +3
Self-Supervised Contrastive Learning has proven effective in deriving high-quality representations from unlabeled data. However, a major challenge that hinders both unimodal and mu…
Enhancing Low-Resource Relation Representations through Multi-View Decoupling
Chenghao Fan, Wei Wei, Xiaoye Qu +4
Recently, prompt-tuning with pre-trained language models (PLMs) has demonstrated the significantly enhancing ability of relation extraction (RE) tasks. However, in low-resource sce…