  Large language models (LLMs) like ChatGPT, Gemini, or LLaMA have been
trending recently, demonstrating considerable advancement and generalizability
power in countless domains. However, LLMs create an even bigger black box
exacerbating opacity, with interpretability limited to few approaches. The
uncertainty and opacity embedded in LLMs' nature restrict their application in
high-stakes domains like financial fraud, phishing, etc. Current approaches
mainly rely on traditional textual classification with posterior interpretable
algorithms, suffering from attackers who may create versatile adversarial
samples to break the system's defense, forcing users to make trade-offs between
efficiency and robustness. To address this issue, we propose a novel cascading
framework called Genshin (General Shield for Natural Language Processing with
Large Language Models), utilizing LLMs as defensive one-time plug-ins. Unlike
most applications of LLMs that try to transform text into something new or
structural, Genshin uses LLMs to recover text to its original state. Genshin
aims to combine the generalizability of the LLM, the discrimination of the
median model, and the interpretability of the simple model. Our experiments on
the task of sentimental analysis and spam detection have shown fatal flaws of
the current median models and exhilarating results on LLMs' recovery ability,
demonstrating that Genshin is both effective and efficient. In our ablation
study, we unearth several intriguing observations. Utilizing the LLM defender,
a tool derived from the 4th paradigm, we have reproduced BERT's 15% optimal
mask rate results in the 3rd paradigm of NLP. Additionally, when employing the
LLM as a potential adversarial tool, attackers are capable of executing
effective attacks that are nearly semantically lossless.
