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We advance the field of Parameter-Efficient Fine-Tuning (PEFT) with our novelmulti-adapter method, OrchMoE, which capitalizes on modular skill architecturefor enhanced forward transfer in neural networks. Unlike prior models thatdepend on explicit task identification inputs, OrchMoE automatically discernstask categories, streamlining the learning process. This is achieved through anintegrated mechanism comprising an Automatic Task Classification module and aTask-Skill Allocation module, which collectively deduce task-specificclassifications and tailor skill allocation matrices. Our extensive evaluationson the 'Super Natural Instructions' dataset, featuring 1,600 diverseinstructional tasks, indicate that OrchMoE substantially outperforms comparablemulti-adapter baselines in terms of both performance and sample utilizationefficiency, all while operating within the same parameter constraints. Thesefindings suggest that OrchMoE offers a significant leap forward in multi-tasklearning efficiency.

OrchMoE: Efficient Multi-Adapter Learning with Task-Skill Synergy