Cover Image

Boosting Large Language Models with Mask Fine-Tuning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zhang, Mingyuan, Bai, Yue, Wang, Huan, Wang, Yizhou, Dong, Qihua, Zhang, Yitian, Fu, Yun
Format:	Preprint
Published:	2025
Subjects:	Computation and Language Artificial Intelligence Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Cautious Next Token Prediction
by: Wang, Yizhou, et al.
Published: (2025)

Don't Judge by the Look: Towards Motion Coherent Video Representation
by: Zhang, Yitian, et al.
Published: (2024)

Crafting Efficient Fine-Tuning Strategies for Large Language Models
by: Oliver, Michael, et al.
Published: (2024)

Improving Large Language Models with Concept-Aware Fine-Tuning
by: Chen, Michael K., et al.
Published: (2025)

Instruction Tuning for Large Language Models: A Survey
by: Zhang, Shengyu, et al.
Published: (2023)

Accessing Vision Foundation Models via ImageNet-1K
by: Zhang, Yitian, et al.
Published: (2024)

Parameter-Efficient Fine-Tuning for Foundation Models
by: Zhang, Dan, et al.
Published: (2025)

An Examination on the Effectiveness of Divide-and-Conquer Prompting in Large Language Models
by: Zhang, Yizhou, et al.
Published: (2024)

BoostStep: Boosting mathematical capability of Large Language Models via improved single-step reasoning
by: Zhang, Beichen, et al.
Published: (2025)

MMICT: Boosting Multi-Modal Fine-Tuning with In-Context Examples
by: Chen, Tao, et al.
Published: (2023)

Scaling Sparse Fine-Tuning to Large Language Models
by: Ansell, Alan, et al.
Published: (2024)

Assessing the Macro and Micro Effects of Random Seeds on Fine-Tuning Large Language Models
by: Bui, Nghia, 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)

Linear Chain Transformation: Expanding Optimization Dynamics for Fine-Tuning Large Language Models
by: Wang, Yulong, et al.
Published: (2024)

BIPEFT: Budget-Guided Iterative Search for Parameter Efficient Fine-Tuning of Large Pretrained Language Models
by: Chang, Aofei, et al.
Published: (2024)

Training-Free Bayesianization for Low-Rank Adapters of Large Language Models
by: Shi, Haizhou, et al.
Published: (2024)

PAT: Pruning-Aware Tuning for Large Language Models
by: Liu, Yijiang, et al.
Published: (2024)

The Janus Interface: How Fine-Tuning in Large Language Models Amplifies the Privacy Risks
by: Chen, Xiaoyi, et al.
Published: (2023)

Hyperbolic Fine-Tuning for Large Language Models
by: Yang, Menglin, et al.
Published: (2024)

Inference-Aware Fine-Tuning for Best-of-N Sampling in Large Language Models
by: Chow, Yinlam, et al.
Published: (2024)

Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning
by: Krishnan, Ranganath, et al.
Published: (2024)

E-Sparse: Boosting the Large Language Model Inference through Entropy-based N:M Sparsity
by: Li, Yun, et al.
Published: (2023)

Parameter-Efficient Fine-Tuning in Large Models: A Survey of Methodologies
by: Wang, Luping, et al.
Published: (2024)

Let the Expert Stick to His Last: Expert-Specialized Fine-Tuning for Sparse Architectural Large Language Models
by: Wang, Zihan, et al.
Published: (2024)

FineSteer: A Unified Framework for Fine-Grained Inference-Time Steering in Large Language Models
by: Weng, Zixuan, et al.
Published: (2026)

AdaZeta: Adaptive Zeroth-Order Tensor-Train Adaption for Memory-Efficient Large Language Models Fine-Tuning
by: Yang, Yifan, et al.
Published: (2024)

PoliTune: Analyzing the Impact of Data Selection and Fine-Tuning on Economic and Political Biases in Large Language Models
by: Agiza, Ahmed, et al.
Published: (2024)

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

LISA: Layerwise Importance Sampling for Memory-Efficient Large Language Model Fine-Tuning
by: Pan, Rui, et al.
Published: (2024)

Self-Generated Critiques Boost Reward Modeling for Language Models
by: Yu, Yue, et al.
Published: (2024)

Re-Tuning: Overcoming the Compositionality Limits of Large Language Models with Recursive Tuning
by: Pasewark, Eric, et al.
Published: (2024)

Selection of LLM Fine-Tuning Data based on Orthogonal Rules
by: Li, Xiaomin, et al.
Published: (2024)

Fine-Tuning Language Models with Reward Learning on Policy
by: Lang, Hao, et al.
Published: (2024)

Fine-Tuning Improves Information Conveyance in Language Models
by: Cheng, Yuwei, et al.
Published: (2026)

SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models
by: Wang, Xiaoxuan, et al.
Published: (2023)

Are Large Language Models Table-based Fact-Checkers?
by: Zhang, Hanwen, et al.
Published: (2024)

Selective Self-Rehearsal: A Fine-Tuning Approach to Improve Generalization in Large Language Models
by: Gupta, Sonam, et al.
Published: (2024)

QLESS: A Quantized Approach for Data Valuation and Selection in Large Language Model Fine-Tuning
by: Ananta, Moses, et al.
Published: (2025)

MaskLLM: Learnable Semi-Structured Sparsity for Large Language Models
by: Fang, Gongfan, et al.
Published: (2024)

Long Exposure: Accelerating Parameter-Efficient Fine-Tuning for LLMs under Shadowy Sparsity
by: Wang, Tuowei, et al.
Published: (2025)