Summary of Safeworld: Geo-diverse Safety Alignment, by Da Yin et al.
SafeWorld: Geo-Diverse Safety Alignment
by Da Yin, Haoyi Qiu, Kung-Hsiang Huang, Kai-Wei Chang, Nanyun Peng
First submitted to arxiv on: 9 Dec 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 abstract presents a novel benchmark called SafeWorld, designed to evaluate Large Language Models’ (LLMs) ability to generate culturally sensitive and legally compliant responses across diverse global contexts. The benchmark comprises 2,342 test user queries grounded in human-verified cultural norms and legal policies from 50 countries and 493 regions/races. The authors propose a multi-dimensional automatic safety evaluation framework assessing response contextual appropriateness, accuracy, and comprehensiveness. They also synthesize preference pairs for Direct Preference Optimization (DPO) alignment training to enhance LLMs’ alignment with geo-diverse safety standards. The trained SafeWorldLM outperforms competing models, including GPT-4o, on all three evaluation dimensions by a large margin. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are important tools that can help us communicate and work together effectively. But they need to be safe and respectful of different cultures and laws around the world. To test how well these models do in this area, the authors created a new benchmark called SafeWorld. This benchmark includes many questions and examples from 50 countries and 493 regions/races, which helps make sure the language models understand cultural norms and legal policies. The authors also developed a special way to evaluate the safety of the responses given by these language models. They found that current language models struggle to meet these criteria, but they were able to train a model called SafeWorldLM that does much better. |
Keywords
» Artificial intelligence » Alignment » Gpt » Optimization
