While large language models (LLMs) exhibit remarkable capabilities across a
wide range of tasks, they pose potential safety concerns, such as the
``jailbreak'' problem, wherein malicious instructions can manipulate LLMs to
exhibit undesirable behavior. Although several preventive measures have been
developed to mitigate the potential risks associated with LLMs, they have
primarily focused on English data. In this study, we reveal the presence of
multilingual jailbreak challenges within LLMs and consider two potential risk
scenarios: unintentional and intentional. The unintentional scenario involves
users querying LLMs using non-English prompts and inadvertently bypassing the
safety mechanisms, while the intentional scenario concerns malicious users
combining malicious instructions with multilingual prompts to deliberately
attack LLMs. The experimental results reveal that in the unintentional
scenario, the rate of unsafe content increases as the availability of languages
decreases. Specifically, low-resource languages exhibit three times the
likelihood of encountering harmful content compared to high-resource languages,
with both ChatGPT and GPT-4. In the intentional scenario, multilingual prompts
can exacerbate the negative impact of malicious instructions, with
astonishingly high rates of unsafe output: 80.92\% for ChatGPT and 40.71\% for
GPT-4. To handle such a challenge in the multilingual context, we propose a
novel \textsc{Self-Defense} framework that automatically generates multilingual
training data for safety fine-tuning. Experimental results show that ChatGPT
fine-tuned with such data can achieve a substantial reduction in unsafe content
generation. Data is available at
this https URL Warning: This
paper contains examples with potentially harmful content.

大型语言模型（LLMs）存在潜在的安全隐患，因此需要发展预防措施。本研究揭示了 LLMs 内存在的多语言破解挑战，并针对意外和恶意的风险场景进行了探讨。实验结果显示，在多语言环境中，通过自卫框架进行训练可以显著减少 LLMs 生成的不安全内容。

大规模语言模型中的多语言越狱挑战

Multilingual Jailbreak Challenges in Large Language Models

Prompt-based tuning has been proven effective for pretrained language models
(PLMs). While most of the existing work focuses on the monolingual prompts, we
study the multilingual prompts for multilingual PLMs, especially in the
zero-shot cross-lingual setting. To alleviate the effort of designing different
prompts for multiple languages, we propose a novel model that uses a unified
prompt for all languages, called UniPrompt. Different from the discrete prompts
and soft prompts, the unified prompt is model-based and language-agnostic.
Specifically, the unified prompt is initialized by a multilingual PLM to
produce language-independent representation, after which is fused with the text
input. During inference, the prompts can be pre-computed so that no extra
computation cost is needed. To collocate with the unified prompt, we propose a
new initialization method for the target label word to further improve the
model's transferability across languages. Extensive experiments show that our
proposed methods can significantly outperform the strong baselines across
different languages. We release data and code to facilitate future research.

本文提出了一种使用 UniPrompt 的新型模型，其使用单一的多语言 prompt 并且具有语言不可知性，可以显著提高在零样本跨语言设置下的转移性能。