Lifelong prompt tuning has significantly advanced parameter-efficient
lifelong learning with its efficiency and minimal storage demands on various
tasks. Our empirical studies, however, highlights certain transferability
constraints in the current methodologies: a universal algorithm that guarantees
consistent positive transfer across all tasks is currently unattainable,
especially when dealing dissimilar tasks that may engender negative transfer.
Identifying the misalignment between algorithm selection and task specificity
as the primary cause of negative transfer, we present the Similarity Heuristic
Lifelong Prompt Tuning (SHLPT) framework. This innovative strategy partitions
tasks into two distinct subsets by harnessing a learnable similarity metric,
thereby facilitating fruitful transfer from tasks regardless of their
similarity or dissimilarity. Additionally, SHLPT incorporates a parameter pool
to combat catastrophic forgetting effectively. Our experiments shows that SHLPT
outperforms state-of-the-art techniques in lifelong learning benchmarks and
demonstrates robustness against negative transfer in diverse task sequences.

通过利用可学习的相似度度量标准将任务分为两个不同的子集，相似度启发式终身提示调整（SHLPT）框架实现了从任务中实现了有益的传递，同时 SHLPT 还通过参数池有效地对抗灾难性遗忘，实验证明 SHLPT 在终身学习基准测试中优于现有技术并且对多样化任务序列的负面传递具有鲁棒性。