:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Zhang, Hengyuan, Wu, Yanru, Li, Dawei, Yang, Sak, Zhao, Rui, Jiang, Yong, Tan, Fei
Format:	Preprint
Published:	2024
Subjects:	Computation and Language
Online Access:	https://arxiv.org/abs/2404.10306
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Improved Supervised Fine-Tuning for Large Language Models to Mitigate Catastrophic Forgetting
by: Ding, Fei, et al.
Published: (2025)

Model Tailor: Mitigating Catastrophic Forgetting in Multi-modal Large Language Models
by: Zhu, Didi, et al.
Published: (2024)

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

An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning
by: Luo, Yun, et al.
Published: (2023)

Parameter Alignment Mitigates Catastrophic Forgetting in Multilingual Expert Language Models
by: Ahuja, Sanchit, et al.
Published: (2026)

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

Model-Dowser: Data-Free Importance Probing to Mitigate Catastrophic Forgetting in Multimodal Large Language Models
by: Hwang, Hyeontaek, et al.
Published: (2026)

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

Mitigating Catastrophic Forgetting in Continual Learning through Model Growth
by: Süalp, Ege, et al.
Published: (2025)

Sharpness-Aware Pretraining Mitigates Catastrophic Forgetting
by: Watts, Ishaan, et al.
Published: (2026)

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

Mechanistic Analysis of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning
by: Imanov, Olaf Yunus Laitinen
Published: (2026)

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

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

Mitigating Catastrophic Forgetting in Multi-domain Chinese Spelling Correction by Multi-stage Knowledge Transfer Framework
by: Xing, Peng, et al.
Published: (2024)

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

More Than Catastrophic Forgetting: Integrating General Capabilities For Domain-Specific LLMs
by: Liu, Chengyuan, et al.
Published: (2024)

Mitigating Catastrophic Forgetting in Mathematical Reasoning Finetuning through Mixed Training
by: Reynolds, John Graham
Published: (2025)

Understanding Catastrophic Forgetting in Language Models via Implicit Inference
by: Kotha, Suhas, et al.
Published: (2023)

EFUF: Efficient Fine-grained Unlearning Framework for Mitigating Hallucinations in Multimodal Large Language Models
by: Xing, Shangyu, et al.
Published: (2024)

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

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

Mitigating Catastrophic Forgetting in Large Language Models with Forgetting-aware Pruning
by: Huang, Wei, et al.
Published: (2025)

Survey of Specialized Large Language Model
by: Yang, Chenghan, et al.
Published: (2025)

Locate-then-Merge: Neuron-Level Parameter Fusion for Mitigating Catastrophic Forgetting in Multimodal LLMs
by: Yu, Zeping, et al.
Published: (2025)

Conditions for Catastrophic Forgetting in Multilingual Translation
by: Liu, Danni, et al.
Published: (2025)

Mitigating Forgetting in LLM Fine-Tuning via Low-Perplexity Token Learning
by: Wu, Chao-Chung, et al.
Published: (2025)

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

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

A Comparative Empirical Study of Catastrophic Forgetting Mitigation in Sequential Task Adaptation for Continual Natural Language Processing Systems
by: Abrahamyan, Aram, et al.
Published: (2026)

Coarse-to-Fine Highlighting: Reducing Knowledge Hallucination in Large Language Models
by: Lv, Qitan, et al.
Published: (2024)

Catastrophic Forgetting in LLMs: A Comparative Analysis Across Language Tasks
by: Haque, Naimul
Published: (2025)

Towards Coarse-to-Fine Evaluation of Inference Efficiency for Large Language Models
by: Chen, Yushuo, et al.
Published: (2024)

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

Dynamic Expert Specialization: Towards Catastrophic Forgetting-Free Multi-Domain MoE Adaptation
by: Li, Junzhuo, et al.
Published: (2025)

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

BPO: Towards Balanced Preference Optimization between Knowledge Breadth and Depth in Alignment
by: Wang, Sizhe, et al.
Published: (2024)

BA-LoRA: Bias-Alleviating Low-Rank Adaptation to Mitigate Catastrophic Inheritance in Large Language Models
by: Chang, Yupeng, et al.
Published: (2024)

Mitigating Reversal Curse in Large Language Models via Semantic-aware Permutation Training
by: Guo, Qingyan, et al.
Published: (2024)

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