Talking to Yourself: Defying Forgetting in Large Language Models
arXiv:2602.20162v1 Announce Type: cross Abstract: Catastrophic forgetting remains a major challenge when fine-tuning large language models (LLMs) 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 drif
arXiv:2602.20162v1 Announce Type: cross Abstract: Catastrophic forgetting remains a major challenge when fine-tuning large language models (LLMs) 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.
Executive Summary
This study proposes a novel self-augmentation routine, SA-SFT, to mitigate catastrophic forgetting in large language models (LLMs) when fine-tuning on narrow, task-specific data. By generating self-dialogues prior to fine-tuning, SA-SFT maintains performance comparable to the original model and achieves better results in many cases. The approach is lightweight, requiring no external data or additional tuning. The study also presents a theoretical analysis suggesting that style-induced parameter drift contributes to forgetting, and that self-alignment through self-generated data provides an effective countermeasure. The results have significant implications for the development of robust LLMs and highlight the potential of self-augmentation as a simple and effective adaptation mechanism.
Key Points
- ▸ SA-SFT is a lightweight self-augmentation routine that mitigates catastrophic forgetting in LLMs.
- ▸ The approach generates self-dialogues prior to fine-tuning and mixes them with task data.
- ▸ SA-SFT maintains performance comparable to the original model and achieves better results in many cases.
- ▸ The study presents a theoretical analysis suggesting that style-induced parameter drift contributes to forgetting.
Merits
Strength in Robustness
SA-SFT demonstrates robustness to catastrophic forgetting, maintaining performance comparable to the original model in 50 evaluation scenarios.
Efficiency and Simplicity
The approach is lightweight, requiring no external data or additional tuning, making it an attractive adaptation mechanism for LLMs.
Theoretical Insights
The study provides a theoretical analysis of the underlying mechanisms contributing to catastrophic forgetting, shedding light on the role of style-induced parameter drift.
Demerits
Limited Generalizability
The study's findings may not generalize to all LLMs or tasks, and further research is needed to evaluate the approach's effectiveness in diverse scenarios.
Potential Overreliance on Self-Generated Data
The approach may rely too heavily on self-generated data, which could lead to overfitting or reinforce existing biases in the model.
Expert Commentary
The study's contributions are significant, and the proposed self-augmentation routine has the potential to become a standard technique in the field of natural language processing. However, as with any new approach, further research is needed to fully understand its limitations and potential limitations. Additionally, the study's findings highlight the importance of theoretical analysis in understanding the mechanisms underlying catastrophic forgetting, which will be crucial in developing more robust LLMs. Ultimately, this study takes an important step towards addressing a critical challenge in deep learning and has significant implications for the development of robust and reliable LLMs.
Recommendations
- ✓ Further research is needed to evaluate the effectiveness of SA-SFT in diverse scenarios and to explore its potential applications in real-world settings.
- ✓ The study's findings should be replicated and extended to other LLM architectures and tasks to confirm the approach's robustness and generalizability.
