Cover Image

Efficient and Effective Prompt Tuning via Prompt Decomposition and Compressed Outer Product

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Lan, Pengxiang, Xu, Haoyu, Yang, Enneng, Liang, Yuliang, Guo, Guibing, Zhao, Jianzhe, Wang, Xingwei
Format:	Preprint
Published:	2025
Subjects:	Computation and Language Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Efficient Prompt Tuning by Multi-Space Projection and Prompt Fusion
by: Lan, Pengxiang, et al.
Published: (2024)

Distributionally Robust Graph Out-of-Distribution Recommendation via Diffusion Model
by: Zhao, Chu, et al.
Published: (2025)

Graph Representation Learning via Causal Diffusion for Out-of-Distribution Recommendation
by: Zhao, Chu, et al.
Published: (2024)

Symmetric Graph Contrastive Learning against Noisy Views for Recommendation
by: Zhao, Chu, et al.
Published: (2024)

Causal Direct Preference Optimization for Distributionally Robust Generative Recommendation
by: Zhao, Chu, et al.
Published: (2026)

ECHO: Entropy-Confidence Hybrid Optimization for Test-Time Reinforcement Learning
by: Zhao, Chu, et al.
Published: (2026)

Causal Negative Sampling via Diffusion Model for Out-of-Distribution Recommendation
by: Zhao, Chu, et al.
Published: (2025)

Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities
by: Yang, Enneng, et al.
Published: (2024)

Text as a Universal Interface for Transferable Personalization
by: Liu, Yuting, et al.
Published: (2026)

EFPC: Towards Efficient and Flexible Prompt Compression
by: Cao, Yun-Hao, et al.
Published: (2025)

RAG-based Crowdsourcing Task Decomposition via Masked Contrastive Learning with Prompts
by: Yang, Jing, et al.
Published: (2024)

Generating Synthetic Datasets for Few-shot Prompt Tuning
by: Guo, Xu, et al.
Published: (2024)

An Empirical Study on Prompt Compression for Large Language Models
by: Zhang, Zheng, et al.
Published: (2025)

ACCEPT: Adaptive Codebook for Composite and Efficient Prompt Tuning
by: Lin, Yu-Chen, et al.
Published: (2024)

Dynamic Compressing Prompts for Efficient Inference of Large Language Models
by: Hu, Jinwu, et al.
Published: (2025)

PromptKD: Distilling Student-Friendly Knowledge for Generative Language Models via Prompt Tuning
by: Kim, Gyeongman, et al.
Published: (2024)

Black-box Prompt Tuning with Subspace Learning
by: Zheng, Yuanhang, et al.
Published: (2023)

PIS: Linking Importance Sampling and Attention Mechanisms for Efficient Prompt Compression
by: Chen, Lizhe, et al.
Published: (2025)

Prompt-SAW: Leveraging Relation-Aware Graphs for Textual Prompt Compression
by: Ali, Muhammad Asif, et al.
Published: (2024)

Trace-of-Thought Prompting: Investigating Prompt-Based Knowledge Distillation Through Question Decomposition
by: McDonald, Tyler, et al.
Published: (2025)

Discrete Prompt Compression with Reinforcement Learning
by: Jung, Hoyoun, et al.
Published: (2023)

Integrating Chemistry Knowledge in Large Language Models via Prompt Engineering
by: Liu, Hongxuan, et al.
Published: (2024)

Dynamic Task Vector Grouping for Efficient Multi-Task Prompt Tuning
by: Zhang, Pieyi, et al.
Published: (2025)

EvoPrompt: Connecting LLMs with Evolutionary Algorithms Yields Powerful Prompt Optimizers
by: Guo, Qingyan, et al.
Published: (2023)

Subgraph-level Universal Prompt Tuning
by: Lee, Junhyun, et al.
Published: (2024)

PAFT: Prompt-Agnostic Fine-Tuning
by: Wei, Chenxing, et al.
Published: (2025)

Adapting LLMs for Efficient Context Processing through Soft Prompt Compression
by: Wang, Cangqing, et al.
Published: (2024)

PromptEmbedder:: Efficient and Transferable Text Embedding via Dual-LLM Soft Prompting
by: Tsai, Yu-Che, et al.
Published: (2026)

DynaPrompt: Dynamic Test-Time Prompt Tuning
by: Xiao, Zehao, et al.
Published: (2025)

Augmenting Sequential Recommendation with Balanced Relevance and Diversity
by: Dang, Yizhou, et al.
Published: (2024)

Self-supervised Hierarchical Representation for Medication Recommendation
by: Liang, Yuliang, et al.
Published: (2024)

Repeated Padding+: Simple yet Effective Data Augmentation Plugin for Sequential Recommendation
by: Dang, Yizhou, et al.
Published: (2024)

Parse Trees Guided LLM Prompt Compression
by: Mao, Wenhao, et al.
Published: (2024)

ICPC: In-context Prompt Compression with Faster Inference
by: Yu, Ziyang, et al.
Published: (2025)

Hard Prompts Made Interpretable: Sparse Entropy Regularization for Prompt Tuning with RL
by: Choi, Yunseon, et al.
Published: (2024)

Soft Begging: Modular and Efficient Shielding of LLMs against Prompt Injection and Jailbreaking based on Prompt Tuning
by: Ostermann, Simon, et al.
Published: (2024)

Representation Surgery for Multi-Task Model Merging
by: Yang, Enneng, et al.
Published: (2024)

Prior Prompt Engineering for Reinforcement Fine-Tuning
by: Taveekitworachai, Pittawat, et al.
Published: (2025)

Automatic Prompt Engineering with No Task Cues and No Tuning
by: Chowdhury, Faisal, et al.
Published: (2026)

Soft Prompt Tuning for Cross-Lingual Transfer: When Less is More
by: Philippy, Fred, et al.
Published: (2024)