Imagen de Portada

Improved Supervised Fine-Tuning for Large Language Models to Mitigate Catastrophic Forgetting

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Ding, Fei, Wang, Baiqiao
Formato:	Preprint
Publicado:	2025
Materias:	Computation and Language Artificial Intelligence
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Revisiting Catastrophic Forgetting in Large Language Model Tuning
por: Li, Hongyu, et al.
Publicado: (2024)

Mitigating Catastrophic Forgetting in Large Language Models with Self-Synthesized Rehearsal
por: Huang, Jianheng, et al.
Publicado: (2024)

Preventing Catastrophic Forgetting: Behavior-Aware Sampling for Safer Language Model Fine-Tuning
por: Pham, Anh, et al.
Publicado: (2025)

CURLoRA: Stable LLM Continual Fine-Tuning and Catastrophic Forgetting Mitigation
por: Fawi, Muhammad
Publicado: (2024)

Keep the General, Inject the Specific: Structured Dialogue Fine-Tuning for Knowledge Injection without Catastrophic Forgetting
por: Hong, Yijie, et al.
Publicado: (2025)

Mitigating Catastrophic Forgetting in Target Language Adaptation of LLMs via Source-Shielded Updates
por: Yamaguchi, Atsuki, et al.
Publicado: (2025)

Analyzing and Reducing Catastrophic Forgetting in Parameter Efficient Tuning
por: Ren, Weijieying, et al.
Publicado: (2024)

Dynamic Orthogonal Continual Fine-tuning for Mitigating Catastrophic Forgettings
por: Zhang, Zhixin, et al.
Publicado: (2025)

Reinforcement Fine-Tuning Naturally Mitigates Forgetting in Continual Post-Training
por: Lai, Song, et al.
Publicado: (2025)

LFTF: Locating First and Then Fine-Tuning for Mitigating Gender Bias in Large Language Models
por: Qin, Zhanyue, et al.
Publicado: (2025)

Noise Augmented Fine Tuning for Mitigating Hallucinations in Large Language Models
por: Khadangi, Afshin, et al.
Publicado: (2025)

MoE-CT: A Novel Approach For Large Language Models Training With Resistance To Catastrophic Forgetting
por: Li, Tianhao, et al.
Publicado: (2024)

SelfAug: Mitigating Catastrophic Forgetting in Retrieval-Augmented Generation via Distribution Self-Alignment
por: Huang, Yuqing, et al.
Publicado: (2025)

Countering Catastrophic Forgetting of Large Language Models for Better Instruction Following via Weight-Space Model Merging
por: Lyu, Mengxian, et al.
Publicado: (2026)

Assessing and Mitigating Data Memorization Risks in Fine-Tuned Large Language Models
por: Ramakrishnan, Badrinath, et al.
Publicado: (2025)

Entropy-Adaptive Fine-Tuning: Resolving Confident Conflicts to Mitigate Forgetting
por: Diao, Muxi, et al.
Publicado: (2026)

Improving Linguistic Diversity of Large Language Models with Possibility Exploration Fine-Tuning
por: Mai, Long, et al.
Publicado: (2024)

Beyond Fine-Tuning: Effective Strategies for Mitigating Hallucinations in Large Language Models for Data Analytics
por: Rumiantsau, Mikhail, et al.
Publicado: (2024)

Analyzing Mitigation Strategies for Catastrophic Forgetting in End-to-End Training of Spoken Language Models
por: Hsiao, Chi-Yuan, et al.
Publicado: (2025)

Phased Instruction Fine-Tuning for Large Language Models
por: Pang, Wei, et al.
Publicado: (2024)

Supervised Fine-Tuning versus Reinforcement Learning: A Study of Post-Training Methods for Large Language Models
por: Jiang, Haitao, et al.
Publicado: (2026)

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

Memorization in Fine-Tuned Large Language Models
por: Savine, Danil
Publicado: (2025)

Model Editing at Scale leads to Gradual and Catastrophic Forgetting
por: Gupta, Akshat, et al.
Publicado: (2024)

Safety-Aware Fine-Tuning of Large Language Models
por: Choi, Hyeong Kyu, et al.
Publicado: (2024)

Dynamic Adaptive Optimization for Effective Sentiment Analysis Fine-Tuning on Large Language Models
por: Ding, Hongcheng, et al.
Publicado: (2024)

Evolutionary Strategies lead to Catastrophic Forgetting in LLMs
por: Abdi, Immanuel, et al.
Publicado: (2026)

Balancing Speciality and Versatility: A Coarse to Fine Framework for Mitigating Catastrophic Forgetting in Large Language Models
por: Zhang, Hengyuan, et al.
Publicado: (2024)

RobustFT: Robust Supervised Fine-tuning for Large Language Models under Noisy Response
por: Luo, Junyu, et al.
Publicado: (2024)

Fine-Tuning or Fine-Failing? Debunking Performance Myths in Large Language Models
por: Barnett, Scott, et al.
Publicado: (2024)

LexInstructEval: Lexical Instruction Following Evaluation for Large Language Models
por: Ren, Huimin, et al.
Publicado: (2025)

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

KnowTuning: Knowledge-aware Fine-tuning for Large Language Models
por: Lyu, Yougang, et al.
Publicado: (2024)

Taiyi: A Bilingual Fine-Tuned Large Language Model for Diverse Biomedical Tasks
por: Luo, Ling, et al.
Publicado: (2023)

Large Language Models for Anomaly Detection in Computational Workflows: from Supervised Fine-Tuning to In-Context Learning
por: Jin, Hongwei, et al.
Publicado: (2024)

Reversing Large Language Models for Efficient Training and Fine-Tuning
por: Gal, Eshed, et al.
Publicado: (2025)

Impact of Fine-Tuning Methods on Memorization in Large Language Models
por: Hou, Jie, et al.
Publicado: (2025)

Supervised Fine Tuning of Large Language Models for Domain Specific Knowledge Graph Construction:A Case Study on Hunan's Historical Celebrities
por: Hao, Junjie, et al.
Publicado: (2025)

Proximal Supervised Fine-Tuning
por: Zhu, Wenhong, et al.
Publicado: (2025)

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