:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Shi, Haizhou, Wang, Yibin, Han, Ligong, Zhang, Huan, Wang, Hao
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Artificial Intelligence Computation and Language
Online Access:	https://arxiv.org/abs/2412.05723
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

BLoB: Bayesian Low-Rank Adaptation by Backpropagation for Large Language Models
by: Wang, Yibin, et al.
Published: (2024)

TokUR: Token-Level Uncertainty Estimation for Large Language Model Reasoning
by: Zhang, Tunyu, et al.
Published: (2025)

Continual Learning of Large Language Models: A Comprehensive Survey
by: Shi, Haizhou, et al.
Published: (2024)

Dist2ill: Distributional Distillation for One-Pass Uncertainty Estimation in Large Language Models
by: Zhao, Yicong, et al.
Published: (2025)

Multiple Choice Learning of Low-Rank Adapters for Language Modeling
by: Letzelter, Victor, et al.
Published: (2025)

Ensembles of Low-Rank Expert Adapters
by: Li, Yinghao, et al.
Published: (2025)

LoRA-Pro: Are Low-Rank Adapters Properly Optimized?
by: Wang, Zhengbo, et al.
Published: (2024)

Finer Parameter Steps for Low-Rank PEFT: A Controlled Study with CP Tensor Adapters
by: Wang, Xinjue, et al.
Published: (2026)

SSR: Socratic Self-Refine for Large Language Model Reasoning
by: Shi, Haizhou, et al.
Published: (2025)

LoRETTA: Low-Rank Economic Tensor-Train Adaptation for Ultra-Low-Parameter Fine-Tuning of Large Language Models
by: Yang, Yifan, et al.
Published: (2024)

Scalable Bayesian Low-Rank Adaptation of Large Language Models via Stochastic Variational Subspace Inference
by: Samplawski, Colin, et al.
Published: (2025)

Train Small, Infer Large: Memory-Efficient LoRA Training for Large Language Models
by: Zhang, Jun, et al.
Published: (2025)

LoLCATs: On Low-Rank Linearizing of Large Language Models
by: Zhang, Michael, et al.
Published: (2024)

Low-Rank Adapters Meet Neural Architecture Search for LLM Compression
by: Muñoz, J. Pablo, et al.
Published: (2025)

zFLoRA: Zero-Latency Fused Low-Rank Adapters
by: Gowda, Dhananjaya, et al.
Published: (2025)

Multimodal Needle in a Haystack: Benchmarking Long-Context Capability of Multimodal Large Language Models
by: Wang, Hengyi, et al.
Published: (2024)

CoRA: Optimizing Low-Rank Adaptation with Common Subspace of Large Language Models
by: Xiao, Xiaojun, et al.
Published: (2024)

SQuat: Subspace-orthogonal KV Cache Quantization
by: Wang, Hao, et al.
Published: (2025)

BBox-Adapter: Lightweight Adapting for Black-Box Large Language Models
by: Sun, Haotian, et al.
Published: (2024)

DIDS: Domain Impact-aware Data Sampling for Large Language Model Training
by: Shi, Weijie, et al.
Published: (2025)

Personas within Parameters: Fine-Tuning Small Language Models with Low-Rank Adapters to Mimic User Behaviors
by: Thakur, Himanshu, et al.
Published: (2025)

Learning Adapter Rank via Symmetry Breaking
by: Doyle, Cooper, et al.
Published: (2025)

Boosting Large Language Models with Mask Fine-Tuning
by: Zhang, Mingyuan, et al.
Published: (2025)

PortLLM: Personalizing Evolving Large Language Models with Training-Free and Portable Model Patches
by: Khan, Rana Muhammad Shahroz, et al.
Published: (2024)

Filter-then-Generate: Large Language Models with Structure-Text Adapter for Knowledge Graph Completion
by: Liu, Ben, et al.
Published: (2024)

Data-driven Clustering and Merging of Adapters for On-device Large Language Models
by: Bohdal, Ondrej, et al.
Published: (2026)

TableTime: Reformulating Time Series Classification as Training-Free Table Understanding with Large Language Models
by: Wang, Jiahao, et al.
Published: (2024)

Memorize and Rank: Elevating Large Language Models for Clinical Diagnosis Prediction
by: Ma, Mingyu Derek, et al.
Published: (2025)

FR-Spec: Accelerating Large-Vocabulary Language Models via Frequency-Ranked Speculative Sampling
by: Zhao, Weilin, et al.
Published: (2025)

Generative Adapter: Contextualizing Language Models in Parameters with A Single Forward Pass
by: Chen, Tong, et al.
Published: (2024)

Scalable Efficient Training of Large Language Models with Low-dimensional Projected Attention
by: Lv, Xingtai, et al.
Published: (2024)

SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models
by: Xiao, Guangxuan, et al.
Published: (2022)

Low-Rank Quantization-Aware Training for LLMs
by: Bondarenko, Yelysei, et al.
Published: (2024)

JumpLoRA: Sparse Adapters for Continual Learning in Large Language Models
by: Dragomir, Alexandra, et al.
Published: (2026)

K-Merge: Online Continual Merging of Adapters for On-device Large Language Models
by: Shenaj, Donald, et al.
Published: (2025)

Reinforcement Learning for Reasoning in Large Language Models with One Training Example
by: Wang, Yiping, et al.
Published: (2025)

Improving Data Efficiency for LLM Reinforcement Fine-tuning Through Difficulty-targeted Online Data Selection and Rollout Replay
by: Sun, Yifan, et al.
Published: (2025)

Recover-LoRA: Data-Free Accuracy Recovery of Degraded Language Models via Low-Rank Adaptation
by: Das, Devleena, et al.
Published: (2025)

Graph Neural Prompting with Large Language Models
by: Tian, Yijun, et al.
Published: (2023)

Shifting Attention to Relevance: Towards the Predictive Uncertainty Quantification of Free-Form Large Language Models
by: Duan, Jinhao, et al.
Published: (2023)