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。
Viraj Shah, Nataniel Ruiz, Forrester Cole +4 authors
ZipLoRA effectively combines independently trained style and subject LoRAs to enhance both subject and style fidelity in generative models.
Yiming Wang, Yu Lin, Xiaodong Zeng +1 authors
MultiLoRA improves multi-task adaptation for large language models by reducing the dominance of top singular vectors in LoRA parameter updates through horizontal scaling and modified initialization.
Chengsong Huang, Qian Liu, Bill Yuchen Lin +3 authors
LoraHub, a framework for combining LoRA modules, enables few-shot cross-task generalization and facilitates community sharing of LRA modules for large language models.
Dawid Jan Kopiczko, Tijmen Blankevoort, Yuki Markus Asano
Vector-based Random Matrix Adaptation (VeRA) reduces the number of trainable parameters by 10x compared to LoRA while maintaining performance, and is demonstrated on benchmarks like GLUE and E2E, showing its utility in instruction-following.
Tao Ge, Jing Hu, Xun Wang +2 authors
The In-context Autoencoder (ICAE) compresses long contexts into memory slots for efficient LLM inference using a combination of pretraining and fine-tuning.
Lingmin Ran, Xiaodong Cun, JiaWei Liu +5 authors
X-Adapter upgrades text-to-image diffusion models like SDXL to work with existing plugins without retraining, through feature remapping and a null-text training strategy.
Tiedong Liu, Bryan Kian Hsiang Low
A fine-tuned LLaMA model, Goat, outperforms GPT-4 in arithmetic tasks through supervised learning and task decomposition.
北京市昌平区探索星信息技术及软件开发工作室
京ICP备2026059466号