Copertina

Catastrophic Forgetting in LLMs: A Comparative Analysis Across Language Tasks

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autore principale:	Haque, Naimul
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Computation and Language
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Sharing Matters: Analysing Neurons Across Languages and Tasks in LLMs
di: Wang, Weixuan, et al.
Pubblicazione: (2024)

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

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

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

Strategic Prompting for Conversational Tasks: A Comparative Analysis of Large Language Models Across Diverse Conversational Tasks
di: Joshi, Ratnesh Kumar, et al.
Pubblicazione: (2024)

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

Speech-IFEval: Evaluating Instruction-Following and Quantifying Catastrophic Forgetting in Speech-Aware Language Models
di: Lu, Ke-Han, et al.
Pubblicazione: (2025)

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

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

Measuring Catastrophic Forgetting in Cross-Lingual Transfer Paradigms: Exploring Tuning Strategies
di: Koloski, Boshko, et al.
Pubblicazione: (2023)

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

Negation-Induced Forgetting in LLMs
di: Capuano, Francesca, et al.
Pubblicazione: (2025)

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

Attractor Patch Networks: Reducing Catastrophic Forgetting with Routed Low-Rank Patch Experts
di: Shashank
Pubblicazione: (2026)

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

Finetuning LLMs for Comparative Assessment Tasks
di: Raina, Vatsal, et al.
Pubblicazione: (2024)

BDA: Bangla Text Data Augmentation Framework
di: Tariquzzaman, Md., et al.
Pubblicazione: (2024)

OPLoRA: Orthogonal Projection LoRA Prevents Catastrophic Forgetting during Parameter-Efficient Fine-Tuning
di: Xiong, Yifeng, et al.
Pubblicazione: (2025)

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

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