DSN primarily seeks to transfer knowledge to the new coming task from the learned tasks by selecting the affiliated weights of a small set of neurons to be activated, including the reused neurons from prior tasks via neuron-wise masks. And it also transfers possibly valuable knowledge to the earlier tasks via data-free replay.

In our experiments, the proposed method achieved a test classification accuracy of 96.5%

Experiments demonstrate that our approach yields substantial improvements in high-quality and stable speech generation, consistency with the condition factor, and inference efficiency.

Despite these successes, the highly technical nature of financial texts requires domain-specific LLMs to understand complex financial language and concepts effectively.

Bregman divergence for greater adaptability in personalized scenarios.

For all parameter settings, see Appendix B.2. The code of our CRCL will be released on GitHub.