Neural Networks Remember More: The Power of Parameter Isolation and Combination
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866913692367454208 |
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| author | Zeng, Biqing Li, Zehan Ayesh, Aladdin |
| author_facet | Zeng, Biqing Li, Zehan Ayesh, Aladdin |
| contents | Catastrophic forgetting is a pervasive issue for pre-trained language models (PLMs) during continual learning, where models lose previously acquired knowledge when sequentially trained on a series of tasks. The model's ability to retain old tasks is referred to as stability, while its adaptability to new tasks is called plasticity. Therefore, the key to solving this problem is to find a trade-off between the plasticity and stability of the model. To address this issue, in this paper, we propose a novel method to achieve a balance between model stability and plasticity, thereby mitigating catastrophic forgetting. More specifically, our proposed approach leverages parameter isolation and a subsequent combination strategy. Initially, in the training stage, the model adapts to each downstream task via a parameter isolation method to prevent potential interference among different tasks. We then combine all trained parameters, which contain acquired knowledge, using the task arithmetic method and finally apply them to the backbone model. Empirical evaluations on continual language learning benchmarks substantiate the effectiveness of our approach, revealing a marked enhancement over existing state-of-the-art approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_10966 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Neural Networks Remember More: The Power of Parameter Isolation and Combination Zeng, Biqing Li, Zehan Ayesh, Aladdin Computation and Language Artificial Intelligence Catastrophic forgetting is a pervasive issue for pre-trained language models (PLMs) during continual learning, where models lose previously acquired knowledge when sequentially trained on a series of tasks. The model's ability to retain old tasks is referred to as stability, while its adaptability to new tasks is called plasticity. Therefore, the key to solving this problem is to find a trade-off between the plasticity and stability of the model. To address this issue, in this paper, we propose a novel method to achieve a balance between model stability and plasticity, thereby mitigating catastrophic forgetting. More specifically, our proposed approach leverages parameter isolation and a subsequent combination strategy. Initially, in the training stage, the model adapts to each downstream task via a parameter isolation method to prevent potential interference among different tasks. We then combine all trained parameters, which contain acquired knowledge, using the task arithmetic method and finally apply them to the backbone model. Empirical evaluations on continual language learning benchmarks substantiate the effectiveness of our approach, revealing a marked enhancement over existing state-of-the-art approaches. |
| title | Neural Networks Remember More: The Power of Parameter Isolation and Combination |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2502.10966 |