Continual Learning Using Only Large Language Model Prompting

Jiabao Qiu; Zixuan Ke; Bing Liu

COLING 2025main1 citations

Continual Learning Using Only Large Language Model Prompting

Abstract

We introduce CLOB, a novel continual learning (CL) paradigm wherein a large language model (LLM) is regarded as a black box. Learning is done incrementally via only verbal prompting. CLOB does not fine-tune any part of the LLM or add any trainable parameters to it. It is particularly suitable for LLMs that are accessible via APIs. We also propose a new CL technique, called CIS, based on incremental summarization that also overcomes the LLM’s input length limit. Experiments show CIS outperforms baselines by a very large margin.

BibTeX

@inproceedings{qiu-etal-2025-continual,
    title = "Continual Learning Using Only Large Language Model Prompting",
    author = "Qiu, Jiabao  and
      Ke, Zixuan  and
      Liu, Bing",
    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://aclanthology.org/2025.coling-main.402/",
    pages = "6014--6023"
}