Summary of A Generative Adversarial Attack For Multilingual Text Classifiers, by Tom Roth et al.
A Generative Adversarial Attack for Multilingual Text Classifiers
by Tom Roth, Inigo Jauregi Unanue, Alsharif Abuadbba, Massimo Piccardi
First submitted to arxiv on: 16 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach is proposed to fine-tune a multilingual paraphrase model with an adversarial objective, enabling it to generate effective adversarial examples against multilingual classifiers. The training objective incorporates pre-trained models to ensure text quality and language consistency. This medium-difficulty summary focuses on the technical aspects of the paper, highlighting the use of vocabulary-mapping matrices for end-to-end differentiability and experimental validation over two multilingual datasets and five languages. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a way to make a machine learning model that can understand many languages generate fake text that can trick other models. The goal is to test how well these models work against each other. To do this, the researchers fine-tuned a special type of model called a paraphrase model. They made sure the generated text was good quality and consistent with different languages. |
Keywords
» Artificial intelligence » Machine learning
