Ensuring the resilience of Large Language Models (LLMs) against malicious
exploitation is paramount, with recent focus on mitigating offensive responses.
Yet, the understanding of cant or dark jargon remains unexplored. This paper
introduces a domain-specific Cant dataset and CantCounter evaluation framework,
employing Fine-Tuning, Co-Tuning, Data-Diffusion, and Data-Analysis stages.
Experiments reveal LLMs, including ChatGPT, are susceptible to cant bypassing
filters, with varying recognition accuracy influenced by question types,
setups, and prompt clues. Updated models exhibit higher acceptance rates for
cant queries. Moreover, LLM reactions differ across domains, e.g., reluctance
to engage in racism versus LGBT topics. These findings underscore LLMs'
understanding of cant and reflect training data characteristics and vendor
approaches to sensitive topics. Additionally, we assess LLMs' ability to
demonstrate reasoning capabilities. Access to our datasets and code is
available at this https URL

大型语言模型对黑暗术语和领域特定语言的理解以及其对灵敏话题的处理方法的影响进行了研究，并发现模型存在对过滤器的绕过问题和对话题的不同反应。

无法说不行？大型语言模型中暗黑行话的测量与推理

Can't say cant? Measuring and Reasoning of Dark Jargons in Large  Language Models

With the development of large language models (LLMs) like the GPT series,
their widespread use across various application scenarios presents a myriad of
challenges. This review initially explores the issue of domain specificity,
where LLMs may struggle to provide precise answers to specialized questions
within niche fields. The problem of knowledge forgetting arises as these LLMs
might find it hard to balance old and new information. The knowledge repetition
phenomenon reveals that sometimes LLMs might deliver overly mechanized
responses, lacking depth and originality. Furthermore, knowledge illusion
describes situations where LLMs might provide answers that seem insightful but
are actually superficial, while knowledge toxicity focuses on harmful or biased
information outputs. These challenges underscore problems in the training data
and algorithmic design of LLMs. To address these issues, it's suggested to
diversify training data, fine-tune models, enhance transparency and
interpretability, and incorporate ethics and fairness training. Future
technological trends might lean towards iterative methodologies, multimodal
learning, model personalization and customization, and real-time learning and
feedback mechanisms. In conclusion, future LLMs should prioritize fairness,
transparency, and ethics, ensuring they uphold high moral and ethical standards
when serving humanity.

当面临领域特定问题时，大语言模型（LLMs）可能会遇到问题，如知识遗忘、知识重复、知识幻觉以及知识毒性，这些问题突显了 LLMs 的训练数据和算法设计的困扰。为了解决这些问题，建议对训练数据进行多样化，微调模型，提高透明度和可解释性，并引入伦理和公平性培训。未来的技术趋势可能会倾向于迭代方法学、多模态学习、模型个性化定制以及实时学习和反馈机制。最重要的是，未来的 LLMs 应确保在为人类服务时优先考虑公平、透明和伦理，确保它们持有高的道义和道德标准。