arXiv (All)
open access
PRILoRA: Pruned and Rank-Increasing Low-Rank Adaptation
With the proliferation of large pre-trained language models (PLMs), fine-tuning all model parameters becomes increasingly inefficient, particularly when dealing with numerous downstream tasks that entail substantial training and storage costs. Several approaches aimed at achieving parameter-efficien
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Advantageous Parameter Expansion Training Makes Better Large Language Models#670342
- Advantageous Parameter Expansion Training Makes Better Large Language Models#668277
- GroupDPO: Memory-Efficient Group-Wise Direct Preference Optimization#821839
- Optimization Methods for Personalizing Large Language Models through Retrieval Augmentation#328709
- Selective State-Space Adaptation and Retrieval for Language Model Reasoning#1110303
Record · ID 1141383
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive