Parameter-efficient fine-tuning (PEFT) methods can adapt large language
models to downstream tasks by training a small amount of newly added
parameters. In multi-task settings, PEFT adapters typically train on each task
independently, inhibiting transfer across tasks, or on the concatenation of all
tasks, which can lead to negative interference. To address this, Polytropon
(Ponti et al.) jointly learns an inventory of PEFT adapters and a routing
function to share variable-size sets of adapters across tasks. Subsequently,
adapters can be re-combined and fine-tuned on novel tasks even with limited
data. In this paper, we investigate to what extent the ability to control which
adapters are active for each task leads to sample-efficient generalization.
Thus, we propose less expressive variants where we perform weighted averaging
of the adapters before few-shot adaptation (Poly-mu) instead of learning a
routing function. Moreover, we introduce more expressive variants where
finer-grained task-adapter allocation is learned through a multi-head routing
function (Poly-S). We test these variants on three separate benchmarks for
multi-task learning. We find that Poly-S achieves gains on all three (up to 5.3
points on average) over strong baselines, while incurring a negligible
additional cost in parameter count. In particular, we find that instruction
tuning, where models are fully fine-tuned on natural language instructions for
each task, is inferior to modular methods such as Polytropon and our proposed
variants.

本文提出了 Poly-mu 和 Poly-S 两种新的方法，对比了它们与 Polytropon 方法在多个任务学习中的表现，并发现 Poly-S 方法可以在保持参数少量增加的同时，在三个测试数据集上获得了高达 5.3 个点的平均收益。