Summary of Cfsafety: Comprehensive Fine-grained Safety Assessment For Llms, by Zhihao Liu et al.
CFSafety: Comprehensive Fine-grained Safety Assessment for LLMs
by Zhihao Liu, Chenhui Hu
First submitted to arxiv on: 29 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 A novel benchmark for assessing the safety of large language models (LLMs) is proposed in this paper. The CFSafety benchmark integrates five classic safety scenarios and five types of instruction attacks to evaluate the natural language generation capabilities of LLMs. Eight popular LLMs, including the GPT series, were tested using this benchmark, revealing that while GPT-4 demonstrated superior safety performance, there is still room for improvement in the safety effectiveness of these models. The study’s findings highlight the need for rigorous safety assessments to ensure responsible development and deployment of LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are getting smarter, but they can also create biased or harmful content. To keep us safe, we need to test them thoroughly. This paper introduces a special set of questions to help evaluate how well these models will behave in different situations. The results show that some models do better than others at being safe and responsible. We hope this study helps us develop more helpful and trustworthy language models. |
Keywords
» Artificial intelligence » Gpt
