Cover Image

ClusterUCB: Efficient Gradient-Based Data Selection for Targeted Fine-Tuning of LLMs

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Wang, Zige, Zhu, Qi, Mi, Fei, Xu, Minghui, Jin, Ruochun, Yang, Wenjing
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

Data Management For Training Large Language Models: A Survey
by: Wang, Zige, et al.
Published: (2023)

Token Cleaning: Fine-Grained Data Selection for LLM Supervised Fine-Tuning
by: Pang, Jinlong, et al.
Published: (2025)

60 Data Points are Sufficient to Fine-Tune LLMs for Question-Answering
by: Ye, Junjie, et al.
Published: (2024)

GradPruner: Gradient-Guided Layer Pruning Enabling Efficient Fine-Tuning and Inference for LLMs
by: Huang, Wei, et al.
Published: (2026)

TAGCOS: Task-agnostic Gradient Clustered Coreset Selection for Instruction Tuning Data
by: Zhang, Jipeng, et al.
Published: (2024)

Semantic Loss Guided Data Efficient Supervised Fine Tuning for Safe Responses in LLMs
by: Lu, Yuxiao, et al.
Published: (2024)

Get more for less: Principled Data Selection for Warming Up Fine-Tuning in LLMs
by: Kang, Feiyang, et al.
Published: (2024)

RankAdaptor: Hierarchical Rank Allocation for Efficient Fine-Tuning Pruned LLMs via Performance Model
by: Zhou, Changhai, et al.
Published: (2024)

Select2Reason: Efficient Instruction-Tuning Data Selection for Long-CoT Reasoning
by: Yang, Cehao, et al.
Published: (2025)

Parameter-Efficient Fine-Tuning With Adapters
by: Chen, Keyu, et al.
Published: (2024)

Self-Error-Instruct: Generalizing from Errors for LLMs Mathematical Reasoning
by: Yu, Erxin, et al.
Published: (2025)

TS-PEFT: Unveiling Token-Level Redundancy in Parameter-Efficient Fine-Tuning
by: Ma, Dabiao, et al.
Published: (2025)

Position-Aware Parameter Efficient Fine-Tuning Approach for Reducing Positional Bias in LLMs
by: Zhang, Zheng, et al.
Published: (2024)

DEFT: Data Efficient Fine-Tuning for Pre-Trained Language Models via Unsupervised Core-Set Selection
by: Das, Devleena, et al.
Published: (2023)

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

LESS: Selecting Influential Data for Targeted Instruction Tuning
by: Xia, Mengzhou, et al.
Published: (2024)

Difficulty-Based Preference Data Selection by DPO Implicit Reward Gap
by: Qi, Xuan, et al.
Published: (2025)

Parameter-Efficient Fine-Tuning of LLMs with Mixture of Space Experts
by: Zhang, Buze, et al.
Published: (2026)

EBFT: Effective and Block-Wise Fine-Tuning for Sparse LLMs
by: Guo, Song, et al.
Published: (2024)

Beyond QA Pairs: Assessing Parameter-Efficient Fine-Tuning for Fact Embedding in LLMs
by: Ratnakar, Shivam, et al.
Published: (2025)

Efficient Differentially Private Fine-Tuning of LLMs via Reinforcement Learning
by: Khadangi, Afshin, et al.
Published: (2025)

Hallucination Detection in LLMs: Fast and Memory-Efficient Fine-Tuned Models
by: Arteaga, Gabriel Y., et al.
Published: (2024)

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

TACOS: Open Tagging and Comparative Scoring for Instruction Fine-Tuning Data Selection
by: He, Xixiang, et al.
Published: (2025)

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

Quantifying and Mitigating Selection Bias in LLMs: A Transferable LoRA Fine-Tuning and Efficient Majority Voting Approach
by: Guda, Blessed, et al.
Published: (2025)

Diffusion-Inspired Masked Fine-Tuning for Knowledge Injection in Autoregressive LLMs
by: Pan, Xu, et al.
Published: (2025)

Team-Based Self-Play With Dual Adaptive Weighting for Fine-Tuning LLMs
by: Li, Wu, et al.
Published: (2026)

Correlation-Aware Select and Merge Attention for Efficient Fine-Tuning and Context Length Extension
by: Wang, Ning, et al.
Published: (2024)

Fine-Tuning LLMs for Report Summarization: Analysis on Supervised and Unsupervised Data
by: Rallapalli, Swati, et al.
Published: (2025)

Uncertainty-Aware Gradient Signal-to-Noise Data Selection for Instruction Tuning
by: Yuan, Zhihang, et al.
Published: (2026)

Fine-Tuning Causal LLMs for Text Classification: Embedding-Based vs. Instruction-Based Approaches
by: Yousefiramandi, Amirhossein, et al.
Published: (2025)

Scaling Data Diversity for Fine-Tuning Language Models in Human Alignment
by: Song, Feifan, et al.
Published: (2024)

Parameter Efficient Quasi-Orthogonal Fine-Tuning via Givens Rotation
by: Ma, Xinyu, et al.
Published: (2024)

Filter-then-Weight: Online Data Selection and Reweighting for LLM Fine-Tuning
by: Wang, Fangxin, et al.
Published: (2026)

STAR: Constraint LoRA with Dynamic Active Learning for Data-Efficient Fine-Tuning of Large Language Models
by: Zhang, Linhai, et al.
Published: (2024)

GraphGen: Enhancing Supervised Fine-Tuning for LLMs with Knowledge-Driven Synthetic Data Generation
by: Chen, Zihong, et al.
Published: (2025)

NeuronTune: Fine-Grained Neuron Modulation for Balanced Safety-Utility Alignment in LLMs
by: Pan, Birong, et al.
Published: (2025)

Beyond LoRA vs. Full Fine-Tuning: Gradient-Guided Optimizer Routing for LLM Adaptation
by: Tang, Haozhan, et al.
Published: (2026)

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