Trends · 研究趋势
数据来自 Hugging Face 论文的 AI 提取关键词,按月统计研究方向的增长与热度。
Mind Lab, Song Cao, Vic Cao +59 authors
MinT is a managed infrastructure system that enables efficient low-rank adaptation training and serving by keeping base models resident and moving lightweight adapter revisions, scaling across multiple dimensions including large model architectures, reduced storage requirements, and distributed policy management.
Bowen Zhang, Congchao Guo, Geng Yang +17 authors
MiniMax-Speech, an autoregressive Transformer-based TTS model, generates high-quality speech with a learnable speaker encoder that extracts reference speaker features without transcription, achieving SOTA results in voice cloning and supporting various extensions.
Aleksandr Nesterov, Andrey Sakhovskiy, Ivan Sviridov +5 authors
Experiments on a new Russian-language ICD coding dataset using models like BERT, LLaMA with LoRA, and RAG show significant accuracy improvements in automated clinical coding compared to manual annotations.
Zhiyuan Liang, Dongwen Tang, Yuhao Zhou +11 authors
Drag-and-Drop LLMs generate task-specific parameters through prompt-conditioned parameter generation, achieving significant efficiency gains and cross-domain generalization without per-task training.
Justin Zhao, Timothy Wang, Wael Abid +7 authors
LoRA fine-tuning reduces computational resources while maintaining high performance across various tasks and models, and LoRAX allows for efficient deployment of multiple LLMs on a single GPU.
David Bensaïd, Noam Rotstein, Roy Velich +2 authors
SingLoRA, a reformulated low-rank adaptation method, enhances parameter-efficient fine-tuning by learning a single low-rank matrix and its transpose, ensuring stable optimization and reducing parameter count.
Shuai Yang, Yifan Zhou, Ziwei Liu +1 authors
A novel framework adapts image diffusion models for video by generating key frames with hierarchical constraints and propagating them using patch matching and blending, achieving high-quality and temporally-coherent videos.
Tianyu Fu, Yichen You, Zekai Chen +3 authors
Think-at-Hard (TaH) dynamically refines only hard tokens in LLMs using a neural decider and LoRA, improving reasoning performance with minimal additional parameters or iterations.
Sergey Pletenev, Maria Marina, Daniil Moskovskiy +4 authors
Low-rank adaptation (LoRA) effectively updates LLMs with new knowledge but risks degrading performance on external benchmarks due to data bias and overconfidence.
Dan Biderman, Jose Gonzalez Ortiz, Jacob Portes +9 authors
LoRA, a parameter-efficient finetuning method for large language models, underperforms full finetuning in target domains but provides better regularization and maintains diverse generation compared to other techniques.
Yukang Chen, Shengju Qian, Haotian Tang +4 authors
LongLoRA extends LLM context sizes efficiently using sparse local attention and parameter-efficient fine-tuning, demonstrating strong results with minimal computational cost.
Simian Luo, Yiqin Tan, Suraj Patil +6 authors
LCMs enhance text-to-image generation by distilling from LDMs using LoRA for reduced memory and superior quality, and introduce LCM-LoRA as a plug-in accelerator for various tasks.
Yuwei Guo, Ceyuan Yang, Anyi Rao +4 authors
A framework inserts a motion model into existing text-to-image models to enable animation, using video clips to learn motion priors while preserving model domain and diversity.
Tim Dettmers, Artidoro Pagnoni, Ari Holtzman +1 authors
QLoRA enables efficient finetuning of large language models using 4-bit quantization and Low Rank Adapters, achieving high performance with reduced memory usage.
Weiyu Li, Xuanyang Zhang, Zheng Sun +15 authors
Step1X-3D addresses challenges in 3D generation through a hybrid VAE-DiT geometry generator and diffusion-based texture synthesis, achieving state-of-the-art results with an open-source framework.
Qixun Wang, Xu Bai, Haofan Wang +2 authors
InstantID is a diffusion model-based solution for personalized image synthesis that uses a single facial image and integrates with pre-trained models while maintaining high face fidelity and efficiency.
Qi Sun, Edoardo Cetin, Yujin Tang
A self-adaptive framework for large language models uses reinforcement learning to dynamically adjust task-specific components during inference, enhancing adaptability and performance with efficiency.
Jiale Kang, Ziyin Yue, Zheng Zhan +2 authors
Normalized Low-Rank Adaptation stabilizes LoRA training by normalizing down-projection matrices, accelerating convergence and improving performance without extra parameters or inference cost.
Ting Jiang, Shaohan Huang, Shengyue Luo +8 authors
MoRA, a high-rank updating method using square matrices, enhances the ability of large language models to learn and memorize new knowledge, especially in memory-intensive tasks, compared to LoRA.
Wei Chen, Zhiyuan Li, Zhen Guo +1 authors
An on-device Planner-Action framework, utilizing Phi-3 Mini for planning and Octopus for execution, employs model fine-tuning and multi-LoRA training to enhance efficiency and multi-domain handling on resource-constrained devices.
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