@inproceedings{wang-2025-optimizing,
    title = "Optimizing Lifelong Fine-Tuning for Multiple Tasks via Dataless Distribution Replay",
    author = "Wang, Zhenxing",
    editor = "Rambow, Owen  and
      Wanner, Leo  and
      Apidianaki, Marianna  and
      Al-Khalifa, Hend  and
      Eugenio, Barbara Di  and
      Schockaert, Steven",
    booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2025.coling-main.746/",
    pages = "11261--11273",
    abstract = "The recent emergence of various large language models, which can be fine-tuned with minimal instruction data, has demonstrated impressive performance across various tasks. However, a phenomenon of forgetting occurs during life- long fine-tuning because training on new tasks interferes with the previously acquired knowl- edge. To mitigate catastrophic forgetting, con- ventional data replay methods achieve high per- formance, but at the cost of compromising data privacy and security. This paper introduces a dataless distribution replay approach for life- long fine-tuning. Concretely, the distribution distillation is applied to replay the output dis- tribution of the linear layers at previous task stages. The optimal solution for this distri- bution replay can be directly computed using the retained inner product matrix of the input data, thereby eliminating the need for previ- ous data. Additionally, Singular Value Decom- position (SVD) and module accumulation are employed to further enhance the performance of dataless distribution replay method. Finally, the evaluation is conducted in a lifelong fine- tuning scenario involving multiple tasks. The experimental results and analysis show that the proposed method achieves significant improve- ments compared to several strong lifelong fine- tuning methods."
}