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20242026
most citedPhi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs

7 citations · 10 across the 14 of their papers we have counts for

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8 papers · 1 filter

cs.CV2024

Is Your World Simulator a Good Story Presenter? A Consecutive Events-Based Benchmark for Future Long Video Generation

Yiping Wang, Xuehai He, Kuan Wang +5

The current state-of-the-art video generative models can produce commercial-grade videos with highly realistic details. However, they still struggle to coherently present multiple…

cs.CV2024

Mojito: Motion Trajectory and Intensity Control for Video Generation

Xuehai He, Shuohang Wang, Jianwei Yang +7

Recent advancements in diffusion models have shown great promise in producing high-quality video content. However, efficiently training video diffusion models capable of integratin…

cs.CL2024

Temperature-Centric Investigation of Speculative Decoding with Knowledge Distillation

Siru Ouyang, Shuohang Wang, Minhao Jiang +4

Speculative decoding stands as a pivotal technique to expedite inference in autoregressive (large) language models. This method employs a smaller draft model to speculate a block o…

cs.LG2024★ 1 cited

LoRC: Low-Rank Compression for LLMs KV Cache with a Progressive Compression Strategy

Rongzhi Zhang, Kuang Wang, Liyuan Liu +4

The Key-Value (KV) cache is a crucial component in serving transformer-based autoregressive large language models (LLMs), enabling faster inference by storing previously computed K…

cs.CL2024

GRIN: GRadient-INformed MoE

Liyuan Liu, Young Jin Kim, Shuohang Wang +14

Mixture-of-Experts (MoE) models scale more effectively than dense models due to sparse computation through expert routing, selectively activating only a small subset of expert modu…

cs.LG2024

Cost-Effective Proxy Reward Model Construction with On-Policy and Active Learning

Yifang Chen, Shuohang Wang, Ziyi Yang +6

Reinforcement learning with human feedback (RLHF), as a widely adopted approach in current large language model pipelines, is \textit{bottlenecked by the size of human preference d…

cs.LG2024

Self-Exploring Language Models: Active Preference Elicitation for Online Alignment

Shenao Zhang, Donghan Yu, Hiteshi Sharma +6

Preference optimization, particularly through Reinforcement Learning from Human Feedback (RLHF), has achieved significant success in aligning Large Language Models (LLMs) to adhere…

cs.CV2024★ 2 cited

Multi-LoRA Composition for Image Generation

Ming Zhong, Yelong Shen, Shuohang Wang +6

Low-Rank Adaptation (LoRA) is extensively utilized in text-to-image models for the accurate rendition of specific elements like distinct characters or unique styles in generated im…