Low-Rank Adaptation

2 items

RESEARCHarXiv CS.CL·20h ago

GraphLoRA: Structure-Aware Low-Rank Adaptation for Large Language Model Recommendation

GraphLoRA proposes a novel framework for Large Language Model Recommendation (LLMRec) that integrates structural information with textual semantics. It achieves this by embedding a trainable graph message-passing network within the low-rank adaptation pathway, allowing collaborative topology to explicitly guide parameter updates.

Low-Rank Adaptation Graph Neural Networks Recommendation Systems AI Research

RESEARCHarXiv CS.LG·4/15/2026

Polynomial Expansion Rank Adaptation: Enhancing Low-Rank Fine-Tuning with High-Order Interactions

Polynomial Expansion Rank Adaptation (PERA) is a novel method to enhance low-rank adaptation (LoRA) for fine-tuning large language models. It introduces structured polynomial expansion into the low-rank factor space to model richer nonlinear high-order interactions, overcoming LoRA's linear limitations without increasing rank or inference cost.

LLMs Low-Rank Adaptation machine learning Polynomial Expansion