Summary of Slm As Guardian: Pioneering Ai Safety with Small Language Models, by Ohjoon Kwon et al.
SLM as Guardian: Pioneering AI Safety with Small Language Models
by Ohjoon Kwon, Donghyeon Jeon, Nayoung Choi, Gyu-Hwung Cho, Changbong Kim, Hyunwoo Lee, Inho Kang, Sun Kim, Taiwoo Park
First submitted to arxiv on: 30 May 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 ensure the safety of large language models (LLMs) while maintaining their helpfulness. The authors recognize that previous efforts to enhance alignment between LLMs and human safety requirements have led to increased training costs and unintended degradation of performance. To overcome these challenges, they introduce a modular design that employs a smaller LLM to detect harmful user queries. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper suggests a new way to make sure large language models are safe for use while still being helpful. The authors point out that previous attempts to improve the safety of these models have resulted in higher training costs and unintended decreases in performance. To solve this problem, they propose using a smaller version of the model to detect harmful user queries. |
Keywords
» Artificial intelligence » Alignment
