Summary of Fortifying Ethical Boundaries in Ai: Advanced Strategies For Enhancing Security in Large Language Models, by Yunhong He et al.
Fortifying Ethical Boundaries in AI: Advanced Strategies for Enhancing Security in Large Language Models
by Yunhong He, Jianling Qiu, Wei Zhang, Zhengqing Yuan
First submitted to arxiv on: 27 Jan 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 Medium Difficulty summary: This paper addresses the challenges posed by large language models (LLMs) in natural language processing and artificial intelligence. Specifically, it focuses on enhancing the security and ethics of LLMs, including GPT-3.5 and LLaMA-2, which have revolutionized text generation, translation, and question-answering tasks. The authors introduce a multi-pronged approach to prevent unethical responses, detect role-playing attacks, restrict prohibited content generation, and extend these methods to Multi-Model Large Language Models (MLLMs). This approach maintains high performance while fortifying models against privacy breaches and manipulations. State-of-the-art performance is demonstrated under various attack prompts without compromising the model’s core functionalities. The introduction of differentiated security levels empowers users to control their personal data disclosure, contributing to reduced social risks, enhanced data protection, and promoted social equity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper deals with making sure that language models are used in a way that is safe and fair for everyone. These models can be very powerful but also pose some challenges, like responding in ways that are not appropriate or leaking personal information. The authors suggest four methods to address these issues, including filtering out sensitive words, detecting when someone is trying to trick the model, restricting what kind of content can be generated, and making it easier for users to control their own data. This approach helps keep language models safe from being used in a way that could cause harm or invade people’s privacy. |
Keywords
» Artificial intelligence » Gpt » Llama » Natural language processing » Question answering » Text generation » Translation
