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Simplifying large language models with adapters

Published on:

30 October 2023

Primary Category:

Computation and Language

Paper Authors:

Rishabh Bhardwaj,

Tushar Vaidya,

Soujanya Poria

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Key Details

Adapter modules can efficiently adapt large pre-trained language models to new tasks

Tropical geometry helps identify important adapter parameters to preserve when pruning

An optimization method prunes adapters while maintaining hypersurface orientation

Experiments show 60%+ of adapter params can be pruned with little performance drop

A combined magnitude and tropical pruning method works best across tasks

AI generated summary

Simplifying large language models with adapters

This paper proposes using small adapter modules inserted between layers of a large pre-trained language model to adapt it for specific tasks. This allows task-specific adaptation without retraining the full model. The authors develop an approach to prune the adapter modules without hurting performance, by preserving the geometric characteristics of the network.

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