PRISM: Preference-Aware Influence Function Based Data Selection Method for Efficient Fine-Tuning
Fuente:
arXiv
Saved in:
| Main Authors: | Lin, Qihao, Chen, Guanxu, Liu, Dongrui, Shao, Jing |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Beyond External Monitors: Enhancing Transparency of Large Language Models for Easier Monitoring
by: Chen, Guanxu, et al.
Published: (2025)
by: Chen, Guanxu, et al.
Published: (2025)
Rethinking Entropy Regularization in Large Reasoning Models
by: Jiang, Yuxian, et al.
Published: (2025)
by: Jiang, Yuxian, et al.
Published: (2025)
Loop as a Bridge: Can Looped Transformers Truly Link Representation Space and Natural Language Outputs?
by: Chen, Guanxu, et al.
Published: (2026)
by: Chen, Guanxu, et al.
Published: (2026)
Data Selection for LLM Alignment Using Fine-Grained Preferences
by: Zhang, Jia, et al.
Published: (2025)
by: Zhang, Jia, et al.
Published: (2025)
ProDS: Preference-oriented Data Selection for Instruction Tuning
by: Guo, Wenya, et al.
Published: (2025)
by: Guo, Wenya, et al.
Published: (2025)
Influence Functions for Efficient Data Selection in Reasoning
by: Humane, Prateek, et al.
Published: (2025)
by: Humane, Prateek, et al.
Published: (2025)
Influence-Preserving Proxies for Gradient-Based Data Selection in LLM Fine-tuning
by: Chen, Sirui, et al.
Published: (2026)
by: Chen, Sirui, et al.
Published: (2026)
D3: Diversity, Difficulty, and Dependability-Aware Data Selection for Sample-Efficient LLM Instruction Tuning
by: Zhang, Jia, et al.
Published: (2025)
by: Zhang, Jia, et al.
Published: (2025)
Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy Data
by: Tajwar, Fahim, et al.
Published: (2024)
by: Tajwar, Fahim, et al.
Published: (2024)
LOFT: Low-Rank Orthogonal Fine-Tuning via Task-Aware Support Selection
by: Zhao, Lanxin, et al.
Published: (2026)
by: Zhao, Lanxin, et al.
Published: (2026)
ClusterUCB: Efficient Gradient-Based Data Selection for Targeted Fine-Tuning of LLMs
by: Wang, Zige, et al.
Published: (2025)
by: Wang, Zige, et al.
Published: (2025)
One Algorithm, Two Goals: Dual Scoring for Parameter and Data Selection in LLM Fine-Tuning
by: Chen, Xinrui, et al.
Published: (2026)
by: Chen, Xinrui, et al.
Published: (2026)
OPTune: Efficient Online Preference Tuning
by: Chen, Lichang, et al.
Published: (2024)
by: Chen, Lichang, et al.
Published: (2024)
AgentSlimming: Towards Efficient and Cost-Aware Multi-Agent Systems
by: Chen, Yulang, et al.
Published: (2026)
by: Chen, Yulang, et al.
Published: (2026)
Memory-Efficient Fine-Tuning of Transformers via Token Selection
by: Simoulin, Antoine, et al.
Published: (2025)
by: Simoulin, Antoine, et al.
Published: (2025)
Vulnerability-Aware Alignment: Mitigating Uneven Forgetting in Harmful Fine-Tuning
by: Chen, Liang, et al.
Published: (2025)
by: Chen, Liang, et al.
Published: (2025)
Sparse Layer Sharpness-Aware Minimization for Efficient Fine-Tuning
by: Cheng, Yifei, et al.
Published: (2026)
by: Cheng, Yifei, et al.
Published: (2026)
MATES: Model-Aware Data Selection for Efficient Pretraining with Data Influence Models
by: Yu, Zichun, et al.
Published: (2024)
by: Yu, Zichun, et al.
Published: (2024)
The Long-Term Effects of Data Selection in LLM Fine-Tuning
by: Yang, Yuxin, et al.
Published: (2026)
by: Yang, Yuxin, et al.
Published: (2026)
PRISM: Differentially Private Synthetic Data with Structure-Aware Budget Allocation for Prediction
by: Asiaee, Amir, et al.
Published: (2026)
by: Asiaee, Amir, et al.
Published: (2026)
Efficient Differentially Private Fine-Tuning of Diffusion Models
by: Liu, Jing, et al.
Published: (2024)
by: Liu, Jing, et al.
Published: (2024)
Resource-Efficient Federated Fine-Tuning Large Language Models for Heterogeneous Data
by: Liu, Jun, et al.
Published: (2025)
by: Liu, Jun, et al.
Published: (2025)
Feature Weighting Improves Pool-Based Sequential Active Learning for Regression
by: Wu, Dongrui
Published: (2026)
by: Wu, Dongrui
Published: (2026)
Don't Forget the Nonlinearity: Unlocking Activation Functions in Efficient Fine-Tuning
by: Yin, Bo, et al.
Published: (2025)
by: Yin, Bo, et al.
Published: (2025)
MallowsPO: Fine-Tune Your LLM with Preference Dispersions
by: Chen, Haoxian, et al.
Published: (2024)
by: Chen, Haoxian, et al.
Published: (2024)
GD-FPS: Growth-Driven Feedforward Parameter Selection for Efficient Fine-Tuning
by: Yang, Kenneth, et al.
Published: (2025)
by: Yang, Kenneth, et al.
Published: (2025)
Advancing Compositional Awareness in CLIP with Efficient Fine-Tuning
by: Peleg, Amit, et al.
Published: (2025)
by: Peleg, Amit, et al.
Published: (2025)
PREFINE: Preference-Based Implicit Reward and Cost Fine-Tuning for Safety Alignment
by: Verma, Richa, et al.
Published: (2026)
by: Verma, Richa, et al.
Published: (2026)
Sparsity-Aware Low-Rank Representation for Efficient Fine-Tuning of Large Language Models
by: Zhang, Longteng, et al.
Published: (2026)
by: Zhang, Longteng, et al.
Published: (2026)
GateRA: Token-Aware Modulation for Parameter-Efficient Fine-Tuning
by: Ou, Jie, et al.
Published: (2025)
by: Ou, Jie, et al.
Published: (2025)
Meta-Learning Hyperparameters for Parameter Efficient Fine-Tuning
by: Tian, Zichen, et al.
Published: (2026)
by: Tian, Zichen, et al.
Published: (2026)
FusionGen: Feature Fusion-Based Few-Shot EEG Data Generation
by: Chen, Yuheng, et al.
Published: (2025)
by: Chen, Yuheng, et al.
Published: (2025)
LENSLLM: Unveiling Fine-Tuning Dynamics for LLM Selection
by: Zeng, Xinyue, et al.
Published: (2025)
by: Zeng, Xinyue, et al.
Published: (2025)
Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors
by: Ding, Xinyu, et al.
Published: (2025)
by: Ding, Xinyu, et al.
Published: (2025)
Towards Anomaly-Aware Pre-Training and Fine-Tuning for Graph Anomaly Detection
by: Liu, Yunhui, et al.
Published: (2025)
by: Liu, Yunhui, et al.
Published: (2025)
An Empirical Study of the Influence of Adversarial Fine-Tuning on Compressed Neural Networks
by: Thorsteinsson, Hallgrimur, et al.
Published: (2024)
by: Thorsteinsson, Hallgrimur, et al.
Published: (2024)
Position-Aware Parameter Efficient Fine-Tuning Approach for Reducing Positional Bias in LLMs
by: Zhang, Zheng, et al.
Published: (2024)
by: Zhang, Zheng, et al.
Published: (2024)
PRISM: A Geometric Risk Bound that Decomposes Drift into Scale, Shape, and Head
by: Lin, Chieh-Yen, et al.
Published: (2026)
by: Lin, Chieh-Yen, et al.
Published: (2026)
A Semantic-Aware Layer-Freezing Approach to Computation-Efficient Fine-Tuning of Language Models
by: Gu, Jian, et al.
Published: (2024)
by: Gu, Jian, et al.
Published: (2024)
LEAD: Iterative Data Selection for Efficient LLM Instruction Tuning
by: Lin, Xiaotian, et al.
Published: (2025)
by: Lin, Xiaotian, et al.
Published: (2025)
Similar Items
-
Beyond External Monitors: Enhancing Transparency of Large Language Models for Easier Monitoring
by: Chen, Guanxu, et al.
Published: (2025) -
Rethinking Entropy Regularization in Large Reasoning Models
by: Jiang, Yuxian, et al.
Published: (2025) -
Loop as a Bridge: Can Looped Transformers Truly Link Representation Space and Natural Language Outputs?
by: Chen, Guanxu, et al.
Published: (2026) -
Data Selection for LLM Alignment Using Fine-Grained Preferences
by: Zhang, Jia, et al.
Published: (2025) -
ProDS: Preference-oriented Data Selection for Instruction Tuning
by: Guo, Wenya, et al.
Published: (2025)