Evaluating The Susceptibility Of Pre-trained Language Models Via Handcrafted Adversarial Examples

Branch Hezekiah J., Cefalu Jonathan Rodriguez, Mchugh Jeremy, Hujer Leyla, Bahl Aditya, Iglesias Daniel Del Castillo, Heichman Ron, Darwishi Ramesh. Arxiv 2022

[Paper]    
BERT Fine Tuning GPT Model Architecture Pretraining Methods Security Training Techniques Transformer

Recent advances in the development of large language models have resulted in public access to state-of-the-art pre-trained language models (PLMs), including Generative Pre-trained Transformer 3 (GPT-3) and Bidirectional Encoder Representations from Transformers (BERT). However, evaluations of PLMs, in practice, have shown their susceptibility to adversarial attacks during the training and fine-tuning stages of development. Such attacks can result in erroneous outputs, model-generated hate speech, and the exposure of users’ sensitive information. While existing research has focused on adversarial attacks during either the training or the fine-tuning of PLMs, there is a deficit of information on attacks made between these two development phases. In this work, we highlight a major security vulnerability in the public release of GPT-3 and further investigate this vulnerability in other state-of-the-art PLMs. We restrict our work to pre-trained models that have not undergone fine-tuning. Further, we underscore token distance-minimized perturbations as an effective adversarial approach, bypassing both supervised and unsupervised quality measures. Following this approach, we observe a significant decrease in text classification quality when evaluating for semantic similarity.

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