Not All Parameters Are Created Equal: Smart Isolation Boosts Fine-Tuning Performance
Fuente:
arXiv
Guardado en:
| Autores principales: | Wang, Yao, Liang, Di, Peng, Minlong |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Parameter Importance is Not Static: Evolving Parameter Isolation for Supervised Fine-Tuning
por: Lin, Zekai, et al.
Publicado: (2026)
por: Lin, Zekai, et al.
Publicado: (2026)
TRACE: Discovering Task-Specific Parameter via Adaptation-Aware Probing for Continual Fine-Tuning
por: Han, Xiaosong, et al.
Publicado: (2026)
por: Han, Xiaosong, et al.
Publicado: (2026)
All Entities are Not Created Equal: Examining the Long Tail for Ultra-Fine Entity Typing
por: Deshmukh, Advait, et al.
Publicado: (2024)
por: Deshmukh, Advait, et al.
Publicado: (2024)
Why Supervised Fine-Tuning Fails to Learn: A Systematic Study of Incomplete Learning in Large Language Models
por: Xue, Chao, et al.
Publicado: (2026)
por: Xue, Chao, et al.
Publicado: (2026)
Not All Pretraining are Created Equal: Threshold Tuning and Class Weighting for Imbalanced Polarization Tasks in Low-Resource Settings
por: Oguntade, Abass
Publicado: (2026)
por: Oguntade, Abass
Publicado: (2026)
DeCoRL: Decoupling Reasoning Chains via Parallel Sub-Step Generation and Cascaded Reinforcement for Interpretable and Scalable RLHF
por: Gao, Ziyuan, et al.
Publicado: (2025)
por: Gao, Ziyuan, et al.
Publicado: (2025)
Shall Your Data Strategy Work? Perform a Swift Study
por: Peng, Minlong, et al.
Publicado: (2025)
por: Peng, Minlong, et al.
Publicado: (2025)
DPI: Exploiting Parameter Heterogeneity for Interference-Free Fine-Tuning
por: Liu, Xiaoyu, et al.
Publicado: (2026)
por: Liu, Xiaoyu, et al.
Publicado: (2026)
Not All Errors Are Created Equal: ASCoT Addresses Late-Stage Fragility in Efficient LLM Reasoning
por: Zhang, Dongxu, et al.
Publicado: (2025)
por: Zhang, Dongxu, et al.
Publicado: (2025)
Not All Preference Pairs Are Created Equal: A Recipe for Annotation-Efficient Iterative Preference Learning
por: Yang, Sen, et al.
Publicado: (2024)
por: Yang, Sen, et al.
Publicado: (2024)
TriAdaptLoRA: Brain-Inspired Triangular Adaptive Low-Rank Adaptation for Parameter-Efficient Fine-Tuning
por: Liang, Yao, et al.
Publicado: (2025)
por: Liang, Yao, et al.
Publicado: (2025)
Not All Languages are Equal: Insights into Multilingual Retrieval-Augmented Generation
por: Wu, Suhang, et al.
Publicado: (2024)
por: Wu, Suhang, et al.
Publicado: (2024)
Not All Similarities Are Created Equal: Leveraging Data-Driven Biases to Inform GenAI Copyright Disputes
por: Hacohen, Uri, et al.
Publicado: (2024)
por: Hacohen, Uri, et al.
Publicado: (2024)
Not All Correct Answers Are Equal: Why Your Distillation Source Matters
por: Tian, Xiaoyu, et al.
Publicado: (2025)
por: Tian, Xiaoyu, et al.
Publicado: (2025)
Light-PEFT: Lightening Parameter-Efficient Fine-Tuning via Early Pruning
por: Gu, Naibin, et al.
Publicado: (2024)
por: Gu, Naibin, et al.
Publicado: (2024)
Matrix-Transformation Based Low-Rank Adaptation (MTLoRA): A Brain-Inspired Method for Parameter-Efficient Fine-Tuning
por: Liang, Yao, et al.
Publicado: (2024)
por: Liang, Yao, et al.
Publicado: (2024)
Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning
por: Lialin, Vladislav, et al.
Publicado: (2023)
por: Lialin, Vladislav, et al.
Publicado: (2023)
Not All Options Are Created Equal: Textual Option Weighting for Token-Efficient LLM-Based Knowledge Tracing
por: Kim, JongWoo, et al.
Publicado: (2024)
por: Kim, JongWoo, et al.
Publicado: (2024)
Unlocking Parameter-Efficient Fine-Tuning for Low-Resource Language Translation
por: Su, Tong, et al.
Publicado: (2024)
por: Su, Tong, et al.
Publicado: (2024)
From Parameters to Prompts: Understanding and Mitigating the Factuality Gap between Fine-Tuned LLMs
por: Gong, Xuan, et al.
Publicado: (2025)
por: Gong, Xuan, et al.
Publicado: (2025)
Parameter-Efficient Fine-Tuning with Discrete Fourier Transform
por: Gao, Ziqi, et al.
Publicado: (2024)
por: Gao, Ziqi, et al.
Publicado: (2024)
High-Rank Structured Modulation for Parameter-Efficient Fine-Tuning
por: Liu, Yongkang, et al.
Publicado: (2026)
por: Liu, Yongkang, et al.
Publicado: (2026)
Adapt Once, Thrive with Updates: Transferable Parameter-Efficient Fine-Tuning on Evolving Base Models
por: Gu, Naibin, et al.
Publicado: (2025)
por: Gu, Naibin, et al.
Publicado: (2025)
Parameter-Efficient Fine-Tuning With Adapters
por: Chen, Keyu, et al.
Publicado: (2024)
por: Chen, Keyu, et al.
Publicado: (2024)
Not All Contexts Are Equal: Teaching LLMs Credibility-aware Generation
por: Pan, Ruotong, et al.
Publicado: (2024)
por: Pan, Ruotong, et al.
Publicado: (2024)
Deconfounded Causality-aware Parameter-Efficient Fine-Tuning for Problem-Solving Improvement of LLMs
por: Wang, Ruoyu, et al.
Publicado: (2024)
por: Wang, Ruoyu, et al.
Publicado: (2024)
Boosting Large Language Models with Mask Fine-Tuning
por: Zhang, Mingyuan, et al.
Publicado: (2025)
por: Zhang, Mingyuan, et al.
Publicado: (2025)
Parameter-Efficient Fine-Tuning for Foundation Models
por: Zhang, Dan, et al.
Publicado: (2025)
por: Zhang, Dan, et al.
Publicado: (2025)
Parameter-Efficient Fine-Tuning of Large Language Models using Semantic Knowledge Tuning
por: Prottasha, Nusrat Jahan, et al.
Publicado: (2024)
por: Prottasha, Nusrat Jahan, et al.
Publicado: (2024)
SIBO: A Simple Booster for Parameter-Efficient Fine-Tuning
por: Wen, Zhihao, et al.
Publicado: (2024)
por: Wen, Zhihao, et al.
Publicado: (2024)
All Claims Are Equal, but Some Claims Are More Equal Than Others: Importance-Sensitive Factuality Evaluation of LLM Generations
por: Wanner, Miriam, et al.
Publicado: (2025)
por: Wanner, Miriam, et al.
Publicado: (2025)
SPP: Sparsity-Preserved Parameter-Efficient Fine-Tuning for Large Language Models
por: Lu, Xudong, et al.
Publicado: (2024)
por: Lu, Xudong, et al.
Publicado: (2024)
IndicLLMSuite: A Blueprint for Creating Pre-training and Fine-Tuning Datasets for Indian Languages
por: Khan, Mohammed Safi Ur Rahman, et al.
Publicado: (2024)
por: Khan, Mohammed Safi Ur Rahman, et al.
Publicado: (2024)
Fine-Tuning Small Embeddings for Elevated Performance
por: Silwal, Biraj
Publicado: (2024)
por: Silwal, Biraj
Publicado: (2024)
Not All Proofs Are Equal: Evaluating LLM Proof Quality Beyond Correctness
por: Petrov, Ivo, et al.
Publicado: (2026)
por: Petrov, Ivo, et al.
Publicado: (2026)
The Fine-Tuning Paradox: Boosting Translation Quality Without Sacrificing LLM Abilities
por: Stap, David, et al.
Publicado: (2024)
por: Stap, David, et al.
Publicado: (2024)
Analyzing the Effects of Supervised Fine-Tuning on Model Knowledge from Token and Parameter Levels
por: Ye, Junjie, et al.
Publicado: (2025)
por: Ye, Junjie, et al.
Publicado: (2025)
Parameter-Efficient Fine-Tuning via Circular Convolution
por: Chen, Aochuan, et al.
Publicado: (2024)
por: Chen, Aochuan, et al.
Publicado: (2024)
Parameter-Efficient Fine-Tuning of State Space Models
por: Galim, Kevin, et al.
Publicado: (2024)
por: Galim, Kevin, et al.
Publicado: (2024)
PEFT-Bench: A Parameter-Efficient Fine-Tuning Methods Benchmark
por: Belanec, Robert, et al.
Publicado: (2025)
por: Belanec, Robert, et al.
Publicado: (2025)
Ejemplares similares
-
Parameter Importance is Not Static: Evolving Parameter Isolation for Supervised Fine-Tuning
por: Lin, Zekai, et al.
Publicado: (2026) -
TRACE: Discovering Task-Specific Parameter via Adaptation-Aware Probing for Continual Fine-Tuning
por: Han, Xiaosong, et al.
Publicado: (2026) -
All Entities are Not Created Equal: Examining the Long Tail for Ultra-Fine Entity Typing
por: Deshmukh, Advait, et al.
Publicado: (2024) -
Why Supervised Fine-Tuning Fails to Learn: A Systematic Study of Incomplete Learning in Large Language Models
por: Xue, Chao, et al.
Publicado: (2026) -
Not All Pretraining are Created Equal: Threshold Tuning and Class Weighting for Imbalanced Polarization Tasks in Low-Resource Settings
por: Oguntade, Abass
Publicado: (2026)