IDANI: Inference-time Domain Adaptation via Neuron-level Interventions
- URL: http://arxiv.org/abs/2206.00259v1
- Date: Wed, 1 Jun 2022 06:39:28 GMT
- Title: IDANI: Inference-time Domain Adaptation via Neuron-level Interventions
- Authors: Omer Antverg, Eyal Ben-David, Yonatan Belinkov
- Abstract summary: We propose a new approach for domain adaptation (DA), using neuron-level interventions.
We modify the representation of each test example in specific neurons, resulting in a counterfactual example from the source domain.
Our experiments show that our method improves performance on unseen domains.
- Score: 24.60778570114818
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Large pre-trained models are usually fine-tuned on downstream task data, and
tested on unseen data. When the train and test data come from different
domains, the model is likely to struggle, as it is not adapted to the test
domain. We propose a new approach for domain adaptation (DA), using
neuron-level interventions: We modify the representation of each test example
in specific neurons, resulting in a counterfactual example from the source
domain, which the model is more familiar with. The modified example is then fed
back into the model. While most other DA methods are applied during training
time, ours is applied during inference only, making it more efficient and
applicable. Our experiments show that our method improves performance on unseen
domains.
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