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

TL;DR

This paper investigates how large language models understand and respond to dark jargon or cant, revealing vulnerabilities and domain-specific behaviors through a new dataset and evaluation framework.

Contribution

Introduces a domain-specific cant dataset and evaluation framework, analyzing LLM susceptibility to dark jargon and their reasoning capabilities across topics.

Findings

LLMs, including ChatGPT, can bypass filters for dark jargon.

Recognition accuracy varies with question types and prompts.

Models show different responses across sensitive domains.

Abstract

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 https://github.com/cistineup/CantCounter.