@inproceedings{wang-etal-2025-mixture,
    title = "Mixture of {L}o{RA} Experts for Continual Information Extraction with {LLM}s",
    author = "Wang, Zitao  and
      Wang, Xinyi  and
      Hu, Wei",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/name-variant-enfa-fane/2025.findings-emnlp.718/",
    doi = "10.18653/v1/2025.findings-emnlp.718",
    pages = "13324--13339",
    ISBN = "979-8-89176-335-7",
    abstract = "We study continual information extraction (IE), which aims to extract emerging information across diverse IE tasks incessantly while avoiding forgetting. Existing approaches are either task-specialized for a single IE task or suffer from catastrophic forgetting and insufficient knowledge transfer in continual IE. This paper proposes a new continual IE model using token-level mixture of LoRA experts with LLMs. We leverage a LoRA router to route each token to the most relevant LoRA experts, facilitating effective knowledge transfer among IE tasks. We guide task experts' selection by task keys to retain the IE task-specific knowledge and mitigate catastrophic forgetting. We design a gate reflection method based on knowledge distillation to address forgetting in the LoRA router and task keys. The experimental results show that our model achieves state-of-the-art performance, effectively mitigating catastrophic forgetting and enhancing knowledge transfer in continual IE."
}