Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.00718 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917655352442880 |
|---|---|
| author | Ji, Xu Zhang, Jianyi Zhou, Ziyin Zhao, Zhangchi Qiao, Qianqian Han, Kaiying Hossen, Md Imran Hei, Xiali |
| author_facet | Ji, Xu Zhang, Jianyi Zhou, Ziyin Zhao, Zhangchi Qiao, Qianqian Han, Kaiying Hossen, Md Imran Hei, Xiali |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_00718 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Can't say cant? Measuring and Reasoning of Dark Jargons in Large Language Models Ji, Xu Zhang, Jianyi Zhou, Ziyin Zhao, Zhangchi Qiao, Qianqian Han, Kaiying Hossen, Md Imran Hei, Xiali Computation and Language Artificial Intelligence 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. |
| title | Can't say cant? Measuring and Reasoning of Dark Jargons in Large Language Models |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2405.00718 |