Transfer learning from pretrained language models recently became the dominant approach for solving many NLP tasks. While fine-tuning large language models usually gives the best performance, in many applications it is preferable to tune much smaller sets of parameters, so that the majority of parameters can be shared across multiple tasks . The main approach is to train one or more task-specific layers on top of the language model. In this paper  we present an alternative approach based on adversarial reprogramming, which extends earlier work on automatic prompt generation. It attempts to learn task-specific word embeddings that, when concatenated to the input text, instruct the language model to solve the specified task. We show that this approach outperforms other methods with a similar number of trainable parameters on SST-2 and MNLI datasets. On SST-2, the performance of our model is comparable to the fully fine-tuned baseline, while on MNLI it is the best among the methods that do not modify the parameters of the body of the language model .
Transfer learning from pretrained language models recently became the dominant approach for solving many NLP tasks. A common approach to transfer learning for multiple tasks that maximize parameter sharing trains one or more task-specific layers on top of the language model. In this paper , we present an alternative approach based on adversarial reprogramming, which extends earlier work on automatic prompt generation. Adversarial reprogramming attempts to learn task-specific word embeddings that, when concatenated to the input text, instruct the language model to solve the specified task. Using up to 25K trainable parameters per task, this approach outperforms all existing methods with up to 25M trainable parameters on the public leaderboard of the GLUE benchmark. Our method, initialized with task-specific human-readable prompts, also works in a few-shot setting, outperforming GPT-3 on two SuperGLUE tasks with just 32 training samples .
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While fine-tuning large language models usually gives the best performance, in many applications it is preferable to tune much smaller sets of parameters, so that the majority of parameters can be shared across multiple tasks . The main approach is to train
A common approach to transfer learning for multiple tasks that maximize parameter sharing trains
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Adversarial reprogramming
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We show that
Using up to 25K trainable parameters per task,
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other methods with a similar number of
all existing methods with up to 25M
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SST-2 and MNLI datasets. On SST-2, the performance of our model is comparable to the fully fine-tuned baseline, while on MNLI it is the best among the methods that do not modify the parameters of the body of the language model
the public leaderboard of the GLUE benchmark. Our method, initialized with task-specific human-readable prompts, also works in a few-shot setting, outperforming GPT-3 on two SuperGLUE tasks with just 32 training samples