1 citations · 2 across the 17 of their papers we have counts for
17 papers
Memory Augmentation Unlocks Efficient Chain-of-Thought Reasoning
Simeng Zhang, Yilong Chen, Wenyuan Zhang +4
Large language models often rely on Chain-of-Thought (CoT) reasoning to solve complex tasks, but verbose reasoning traces introduce substantial inference overhead. CoT compression…
Test-Time Curriculum for Open-Set AIGC Detection
Yiqian Zhang, Zheyuan Gu, Xiangzhao Hao +8
AI-generated image detectors deployed in open-world environments inevitably face distribution shifts as new and stronger generative models continue to emerge. Although existing met…
Memento: Reconstruct to Remember for Consistent Long Video Generation
Xuan Wei, Longbin Ji, Guan Wang +5
Long-form video generation requires recurring subjects to remain consistent across various shots, viewpoints, motions, and scene transitions. Existing temporal decomposition method…
Native Audio-Visual Alignment for Generation
Longbin Ji, Guan Wang, Xuan Wei +6
Joint audio-video generation aims to synthesize temporally synchronized and semantically coherent visual-acoustic content. However, existing open-source methods mainly rely on eith…
Sparse Growing Transformer: Training-Time Sparse Depth Allocation via Progressive Attention Looping
Yao Chen, Yilong Chen, Yinqi Yang +9
Existing approaches to increasing the effective depth of Transformers predominantly rely on parameter reuse, extending computation through recursive execution. Under this paradigm,…
Mixture of Universal Experts: Scaling Virtual Width via Depth-Width Transformation
Yilong Chen, Naibin Gu, Junyuan Shang +8
Mixture-of-Experts (MoE) decouples model capacity from per-token computation, yet their scalability remains limited by the physical dimensions of depth and width. To overcome this,…
V-ITI: Mitigating Hallucinations in Multimodal Large Language Models via Visual Inference-Time Intervention
Nan Sun, Zhenyu Zhang, Xixun Lin +8
Multimodal Large Language Models (MLLMs) excel in numerous vision-language tasks yet suffer from hallucinations, producing content inconsistent with input visuals, that undermine r…
Blink: Dynamic Visual Token Resolution for Enhanced Multimodal Understanding
Yuchen Feng, Zhenyu Zhang, Naibin Gu +8
Multimodal large language models (MLLMs) have achieved remarkable progress on various vision-language tasks, yet their visual perception remains limited. Humans, in comparison, per…
Elastic MoE: Unlocking the Inference-Time Scalability of Mixture-of-Experts
Naibin Gu, Zhenyu Zhang, Yuchen Feng +8
Mixture-of-Experts (MoE) models typically fix the number of activated experts at both training and inference. However, real-world deployments often face heterogeneous hardware,…
Advantageous Parameter Expansion Training Makes Better Large Language Models
Naibin Gu, Yilong Chen, Zhenyu Zhang +7
Although scaling up the number of trainable parameters can effectively improve the training performance of large language models, it also leads to increased computational overhead.…
Inner Thinking Transformer: Leveraging Dynamic Depth Scaling to Foster Adaptive Internal Thinking
Yilong Chen, Junyuan Shang, Zhenyu Zhang +7
Large language models (LLMs) face inherent performance bottlenecks under parameter constraints, particularly in processing critical tokens that demand complex reasoning. Empirical…
BeamLoRA: Beam-Constraint Low-Rank Adaptation
Naibin Gu, Zhenyu Zhang, Xiyu Liu +7
Due to the demand for efficient fine-tuning of large language models, Low-Rank Adaptation (LoRA) has been widely adopted as one of the most effective parameter-efficient fine-tunin…
MoR: Mixture of Ranks for Low-Rank Adaptation Tuning
Chuanyu Tang, Yilong Chen, Zhenyu Zhang +4
Low-Rank Adaptation (LoRA) drives research to align its performance with full fine-tuning. However, significant challenges remain: (1) Simply increasing the rank size of LoRA does…
Upcycling Instruction Tuning from Dense to Mixture-of-Experts via Parameter Merging
Tingfeng Hui, Zhenyu Zhang, Shuohuan Wang +3
Mixture-of-Experts (MoE) shines brightly in large language models (LLMs) and demonstrates outstanding performance in plentiful natural language processing tasks. However, existing…
NACL: A General and Effective KV Cache Eviction Framework for LLMs at Inference Time
Yilong Chen, Guoxia Wang, Junyuan Shang +7
Large Language Models (LLMs) have ignited an innovative surge of AI applications, marking a new era of exciting possibilities equipped with extended context windows. However, hosti…
DHA: Learning Decoupled-Head Attention from Transformer Checkpoints via Adaptive Heads Fusion
Yilong Chen, Linhao Zhang, Junyuan Shang +4
Large language models (LLMs) with billions of parameters demonstrate impressive performance. However, the widely used Multi-Head Attention (MHA) in LLMs incurs substantial computat…
HFT: Half Fine-Tuning for Large Language Models
Tingfeng Hui, Zhenyu Zhang, Shuohuan Wang +3
Large language models (LLMs) with one or more fine-tuning phases have become a necessary step to unlock various capabilities, enabling LLMs to follow natural language instructions…