Cover Image

Mitigating Catastrophic Forgetting in Target Language Adaptation of LLMs via Source-Shielded Updates

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Yamaguchi, Atsuki, Morishita, Terufumi, Villavicencio, Aline, Aletras, Nikolaos
Format:	Preprint
Published:	2025
Subjects:	Computation and Language Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Adapting Chat Language Models Using Only Target Unlabeled Language Data
by: Yamaguchi, Atsuki, et al.
Published: (2024)

How Can We Effectively Expand the Vocabulary of LLMs with 0.01GB of Target Language Text?
by: Yamaguchi, Atsuki, et al.
Published: (2024)

An Empirical Study on Cross-lingual Vocabulary Adaptation for Efficient Language Model Inference
by: Yamaguchi, Atsuki, et al.
Published: (2024)

Enhancing Reasoning Capabilities of LLMs via Principled Synthetic Logic Corpus
by: Morishita, Terufumi, et al.
Published: (2024)

Enhancing Linguistic Competence of Language Models through Pre-training with Language Learning Tasks
by: Yamaguchi, Atsuki, et al.
Published: (2026)

Comparing Explanation Faithfulness between Multilingual and Monolingual Fine-tuned Language Models
by: Zhao, Zhixue, et al.
Published: (2024)

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

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

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

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

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

Incorporating Attribution Importance for Improving Faithfulness Metrics
by: Zhao, Zhixue, et al.
Published: (2023)

Investigating Hallucinations in Pruned Large Language Models for Abstractive Summarization
by: Chrysostomou, George, et al.
Published: (2023)

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

How to Alleviate Catastrophic Forgetting in LLMs Finetuning? Hierarchical Layer-Wise and Element-Wise Regularization
by: Song, Shezheng, et al.
Published: (2025)

Is Micro Domain-Adaptive Pre-Training Effective for Real-World Operations? Multi-Step Evaluation Reveals Potential and Bottlenecks
by: Tsunokake, Masaya, et al.
Published: (2026)

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

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

Where does output diversity collapse in post-training?
by: Karouzos, Constantinos, et al.
Published: (2026)

An Empirical Study on Preference Tuning Generalization and Diversity Under Domain Shift
by: Karouzos, Constantinos, et al.
Published: (2026)

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

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

Self-Distillation as a Performance Recovery Mechanism for LLMs: Counteracting Compression and Catastrophic Forgetting
by: Liu, Chi, et al.
Published: (2026)

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

Enhancing Idiomatic Representation in Multiple Languages via an Adaptive Contrastive Triplet Loss
by: He, Wei, et al.
Published: (2024)

Analysing Chain of Thought Dynamics: Active Guidance or Unfaithful Post-hoc Rationalisation?
by: Lewis-Lim, Samuel, et al.
Published: (2025)

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

Compliance versus Sensibility: On the Reasoning Controllability in Large Language Models
by: Tan, Xingwei, et al.
Published: (2026)

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

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

Can Similarity-Based Domain-Ordering Reduce Catastrophic Forgetting for Intent Recognition?
by: Mannekote, Amogh, et al.
Published: (2024)

Intelligent Learning Rate Distribution to reduce Catastrophic Forgetting in Transformers
by: Kenneweg, Philip, et al.
Published: (2024)

Lottery Ticket Adaptation: Mitigating Destructive Interference in LLMs
by: Panda, Ashwinee, et al.
Published: (2024)

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

Reasoning Dynamics and the Limits of Monitoring Modality Reliance in Vision-Language Models
by: Villegas, Danae Sánchez, et al.
Published: (2026)

Towards Minimal Targeted Updates of Language Models with Targeted Negative Training
by: Zhang, Lily H., et al.
Published: (2024)

Joint Flashback Adaptation for Forgetting-Resistant Instruction Tuning
by: Zhao, Yukun, et al.
Published: (2025)

Sign of the Times: Evaluating the use of Large Language Models for Idiomaticity Detection
by: Phelps, Dylan, et al.
Published: (2024)

Catastrophic Forgetting Mitigation via Discrepancy-Weighted Experience Replay
by: Xu, Xinrun, et al.
Published: (2025)

Catastrophic Failure of LLM Unlearning via Quantization
by: Zhang, Zhiwei, et al.
Published: (2024)