Summary of Chain Association-based Attacking and Shielding Natural Language Processing Systems, by Jiacheng Huang and Long Chen
Chain Association-based Attacking and Shielding Natural Language Processing Systems
by Jiacheng Huang, Long Chen
First submitted to arxiv on: 12 Nov 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 This paper proposes a novel adversarial attack on natural language processing (NLP) systems by exploiting the comprehension gap between humans and machines. The authors develop a chain association-based attack, leveraging the fact that humans can quickly link Chinese characters through associations, unlike machines. A graph is constructed to build a search space for potential adversarial examples, which are then optimized using a discrete particle swarm algorithm. Experimental results show that advanced NLP models and applications, including large language models, are vulnerable to this attack, while humans remain effective at understanding perturbed text. The paper also explores two methods to mitigate the attack: adversarial training and associative graph-based recovery. This research highlights the importance of understanding human-machine comprehension gaps in designing robust NLP systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine if you could trick a computer into misunderstanding what someone is saying just by using associations between words that are not obvious to machines. That’s basically what this paper does – it shows how to create fake text that computers can’t understand, but humans still can. The researchers use a special graph to find the right way to make the fake text, and then test it on different computer programs. They found that even very good language models got confused by this trick! But surprisingly, humans were able to understand what was meant, even with the fake text. This research helps us understand how computers can be fooled and how we can protect them from these kinds of attacks. |
Keywords
» Artificial intelligence » Natural language processing » Nlp
