Summary of Can’t Say Cant? Measuring and Reasoning Of Dark Jargons in Large Language Models, by Xu Ji et al.
Can’t say cant? Measuring and Reasoning of Dark Jargons in Large Language Models
by Xu Ji, Jianyi Zhang, Ziyin Zhou, Zhangchi Zhao, Qianqian Qiao, Kaiying Han, Md Imran Hossen, Xiali Hei
First submitted to arxiv on: 25 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel approach to understanding and mitigating the use of “cant” or dark jargon in Large Language Models (LLMs). Specifically, it introduces a domain-specific Cant dataset and evaluation framework called CantCounter, which involves fine-tuning, co-tuning, data diffusion, and data analysis. The experiments reveal that LLMs, including ChatGPT, are susceptible to cant bypassing filters, with varying recognition accuracy influenced by question types, setups, and prompt clues. Moreover, the paper assesses LLMs’ ability to demonstrate reasoning capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study shows how Large Language Models (LLMs) can be tricked into responding to secret language or “cant”. The researchers created a special dataset and way of testing called CantCounter. They tested different models, including one called ChatGPT, and found that they can’t always tell when someone is using cant. This depends on the type of question, how it’s asked, and what clues are given. The study also shows that LLMs react differently to certain topics, like racism or LGBTQ+ issues. Overall, this research helps us understand how LLMs process secret language and how they can be used in different ways. |
Keywords
» Artificial intelligence » Diffusion » Fine tuning » Prompt
