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Talking to Yourself: Defying Forgetting in Large Language Models
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Talking to Yourself: Defying Forgetting in Large Language Models

#Large Language Models #Catastrophic Forgetting #SA-SFT #Self-Augmentation #Fine-tuning #Parameter Drift #Self-Alignment #Task-Specific Data

📌 Key Takeaways

  • SA-SFT prevents catastrophic forgetting in LLMs during fine-tuning
  • The method uses self-generated dialogues mixed with task data
  • It outperformed common baselines in 40 out of 50 evaluation scenarios
  • The research suggests forgetting stems from style-induced parameter drift
  • Self-alignment through self-generated data effectively counters this effect

📖 Full Retelling

Researchers led by Yutao Sun from an unspecified institution introduced SA-SFT, a lightweight self-augmentation method for large language models, in a paper submitted to arXiv on January 23, 2026, aiming to solve the persistent problem of catastrophic forgetting that occurs when models are fine-tuned on narrow task-specific data. The research team, which included Mingshuai Chen, Tiancheng Zhao, Phillip Miao, Zilun Zhang, Haozhan Shen, Ruizhe Zhu, and Jianwei Yin, developed this innovative approach to address the significant challenge where LLMs lose their general knowledge and reasoning capabilities when adapted for specific tasks. Their solution represents a breakthrough in maintaining model performance across diverse applications without compromising the model's broader capabilities. The SA-SFT method works by having the language model generate self-dialogues before the fine-tuning process begins, then mixing these self-authored examples with the task-specific training data, all while maintaining the original optimization and training schedules unchanged. Remarkably, this approach requires no external data sources or additional tuning parameters, making it both resource-efficient and practically implementable across various model architectures and applications.

🏷️ Themes

Artificial Intelligence, Machine Learning, Natural Language Processing

📚 Related People & Topics

Catastrophic interference

AI's tendency to abruptly and drastically forget old info after learning new info

Catastrophic interference, also known as catastrophic forgetting, is the tendency of an artificial neural network to abruptly and drastically forget previously learned information upon learning new information. Neural networks are an important part of the connectionist approach to cognitive science....

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Large language model

Type of machine learning model

A large language model (LLM) is a language model trained with self-supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation. The largest and most capable LLMs are generative pre-trained transformers (GPTs) that provide the c...

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Original Source
--> Computer Science > Computation and Language arXiv:2602.20162 [Submitted on 23 Jan 2026] Title: Talking to Yourself: Defying Forgetting in Large Language Models Authors: Yutao Sun , Mingshuai Chen , Tiancheng Zhao , Phillip Miao , Zilun Zhang , Haozhan Shen , Ruizhe Zhu , Jianwei Yin View a PDF of the paper titled Talking to Yourself: Defying Forgetting in Large Language Models, by Yutao Sun and 7 other authors View PDF HTML Abstract: Catastrophic forgetting remains a major challenge when fine-tuning large language models on narrow, task-specific data, often degrading their general knowledge and reasoning abilities. We propose SA-SFT, a lightweight self-augmentation routine in which an LLM generates self-dialogues prior to fine-tuning, and the resulting self-authored data are mixed with task data without modifying optimization or training schedules. Despite requiring no external data or additional tuning, SA-SFT consistently mitigates catastrophic forgetting while improving in-domain performance. Across 50 evaluation scenarios, it maintains performance comparable to the original model and achieves the best results in 40 cases, outperforming common baselines such as layer freezing and external data mixing. Guided by these empirical findings, we further present a theoretical analysis suggesting that forgetting can partly stem from style-induced parameter drift, and that self-alignment through self-generated data provides an effective means to counteract this effect. Overall, our results indicate that self-augmentation offers a simple and effective mechanism for robust LLM adaptation without incurring catastrophic forgetting. Subjects: Computation and Language (cs.CL) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2602.20162 [cs.CL] (or arXiv:2602.20162v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2602.20162 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Yutao Sun [ view email ] [v1] Fri, 23 Jan 2026 14:25:49 UTC (7,830 KB...
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arxiv.org

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