How Should Pre-Trained Language Models Be Fine-Tuned Towards Adversarial Robustness?

• URL: http://arxiv.org/abs/2112.11668v1
• Date: Wed, 22 Dec 2021 05:04:41 GMT
• Title: How Should Pre-Trained Language Models Be Fine-Tuned Towards Adversarial Robustness?
• Authors: Xinhsuai Dong, Luu Anh Tuan, Min Lin, Shuicheng Yan, Hanwang Zhang
• Abstract summary: We propose Robust Informative Fine-Tuning (RIFT) as a novel adversarial fine-tuning method from an information-theoretical perspective. RIFT encourages an objective model to retain the features learned from the pre-trained model throughout the entire fine-tuning process. Experimental results show that RIFT consistently outperforms the state-of-the-arts on two popular NLP tasks.
• Score: 121.57551065856164
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The fine-tuning of pre-trained language models has a great success in many NLP fields. Yet, it is strikingly vulnerable to adversarial examples, e.g., word substitution attacks using only synonyms can easily fool a BERT-based sentiment analysis model. In this paper, we demonstrate that adversarial training, the prevalent defense technique, does not directly fit a conventional fine-tuning scenario, because it suffers severely from catastrophic forgetting: failing to retain the generic and robust linguistic features that have already been captured by the pre-trained model. In this light, we propose Robust Informative Fine-Tuning (RIFT), a novel adversarial fine-tuning method from an information-theoretical perspective. In particular, RIFT encourages an objective model to retain the features learned from the pre-trained model throughout the entire fine-tuning process, whereas a conventional one only uses the pre-trained weights for initialization. Experimental results show that RIFT consistently outperforms the state-of-the-arts on two popular NLP tasks: sentiment analysis and natural language inference, under different attacks across various pre-trained language models.

Related papers

• Fast Adversarial Training against Textual Adversarial Attacks [11.023035222098008]
  We propose a Fast Adversarial Training (FAT) method to improve the model robustness in the synonym-unaware scenario. FAT uses single-step and multi-step gradient ascent to craft adversarial examples in the embedding space. Experiments demonstrate that FAT significantly boosts the robustness of BERT models in the synonym-unaware scenario.
  arXiv  Detail & Related papers  (2024-01-23T03:03:57Z)
• An Emulator for Fine-Tuning Large Language Models using Small Language Models [91.02498576056057]
  We introduce emulated fine-tuning (EFT), a principled and practical method for sampling from a distribution that approximates the result of pre-training and fine-tuning at different scales. We show that EFT enables test-time adjustment of competing behavioral traits like helpfulness and harmlessness without additional training. Finally, a special case of emulated fine-tuning, which we call LM up-scaling, avoids resource-intensive fine-tuning of large pre-trained models by ensembling them with small fine-tuned models.
  arXiv  Detail & Related papers  (2023-10-19T17:57:16Z)
• Improving Pre-trained Language Model Fine-tuning with Noise Stability Regularization [94.4409074435894]
  We propose a novel and effective fine-tuning framework, named Layerwise Noise Stability Regularization (LNSR) Specifically, we propose to inject the standard Gaussian noise and regularize hidden representations of the fine-tuned model. We demonstrate the advantages of the proposed method over other state-of-the-art algorithms including L2-SP, Mixout and SMART.
  arXiv  Detail & Related papers  (2022-06-12T04:42:49Z)
• Exploring the Universal Vulnerability of Prompt-based Learning Paradigm [21.113683206722207]
  We find that prompt-based learning bridges the gap between pre-training and fine-tuning, and works effectively under the few-shot setting. However, we find that this learning paradigm inherits the vulnerability from the pre-training stage, where model predictions can be misled by inserting certain triggers into the text. We explore this universal vulnerability by either injecting backdoor triggers or searching for adversarial triggers on pre-trained language models using only plain text.
  arXiv  Detail & Related papers  (2022-04-11T16:34:10Z)
• Adversarial Training for Improving Model Robustness? Look at Both Prediction and Interpretation [21.594361495948316]
  We propose a novel feature-level adversarial training method named FLAT. FLAT incorporates variational word masks in neural networks to learn global word importance. Experiments show the effectiveness of FLAT in improving the robustness with respect to both predictions and interpretations.
  arXiv  Detail & Related papers  (2022-03-23T20:04:14Z)
• Self-Ensemble Adversarial Training for Improved Robustness [14.244311026737666]
  Adversarial training is the strongest strategy against various adversarial attacks among all sorts of defense methods. Recent works mainly focus on developing new loss functions or regularizers, attempting to find the unique optimal point in the weight space. We devise a simple but powerful emphSelf-Ensemble Adversarial Training (SEAT) method for yielding a robust classifier by averaging weights of history models.
  arXiv  Detail & Related papers  (2022-03-18T01:12:18Z)
• NoiER: An Approach for Training more Reliable Fine-TunedDownstream Task Models [54.184609286094044]
  We propose noise entropy regularisation (NoiER) as an efficient learning paradigm that solves the problem without auxiliary models and additional data. The proposed approach improved traditional OOD detection evaluation metrics by 55% on average compared to the original fine-tuned models.
  arXiv  Detail & Related papers  (2021-08-29T06:58:28Z)
• BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models [51.53936551681613]
  We show that fine-tuning only the bias terms (or a subset of the bias terms) of pre-trained BERT models is competitive with (and sometimes better than) fine-tuning the entire model. They support the hypothesis that finetuning is mainly about exposing knowledge induced by language-modeling training, rather than learning new task-specific linguistic knowledge.
  arXiv  Detail & Related papers  (2021-06-18T16:09:21Z)
• On the Interplay Between Fine-tuning and Sentence-level Probing for Linguistic Knowledge in Pre-trained Transformers [24.858283637038422]
  We study three different pre-trained models: BERT, RoBERTa, and ALBERT. We find that for some probing tasks fine-tuning leads to substantial changes in accuracy. While fine-tuning indeed changes the representations of a pre-trained model, only in very few cases, fine-tuning has a positive effect on probing accuracy.
  arXiv  Detail & Related papers  (2020-10-06T10:54:00Z)
• Exploring Fine-tuning Techniques for Pre-trained Cross-lingual Models via Continual Learning [74.25168207651376]
  Fine-tuning pre-trained language models to downstream cross-lingual tasks has shown promising results. We leverage continual learning to preserve the cross-lingual ability of the pre-trained model when we fine-tune it to downstream tasks. Our methods achieve better performance than other fine-tuning baselines on the zero-shot cross-lingual part-of-speech tagging and named entity recognition tasks.
  arXiv  Detail & Related papers  (2020-04-29T14:07:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.