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

arXiv: https://arxiv.org/abs/2405.00718 · HTML: https://mlsanity.com/paper/2405.00718/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 6 of its 44 references that are in the map.
- First posted on arXiv 25 Apr 2024.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)

## Builds on (most cited first, 6 of 6)

- [A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers](https://mlsanity.com/paper/2105.03011/) (2021)
- [GPT-4 Is Too Smart To Be Safe: Stealthy Chat with LLMs via Cipher](https://mlsanity.com/paper/2308.06463/) (2023)
- [Multi-step Jailbreaking Privacy Attacks on ChatGPT](https://mlsanity.com/paper/2304.05197/) (2023)
- [Can ChatGPT replace StackOverflow? A Study on Robustness and Reliability of Large Language Model Code Generation](https://mlsanity.com/paper/2308.10335/) (2023)
- [Adversarial Demonstration Attacks on Large Language Models](https://mlsanity.com/paper/2305.14950/) (2023)
- [Generating Phishing Attacks using ChatGPT](https://mlsanity.com/paper/2305.05133/) (2023)

## Abstract (arXiv)

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.