CREST：通过聚类引导的跨语言迁移实现通用安全性防护
CREST: Universal Safety Guardrails Through Cluster-Guided Cross-Lingual Transfer
AI总结：
本文提出CREST模型，通过跨语言迁移有效提升低资源语言的安全性防护，展示其在六个安全基准上的优越表现。
摘要：
确保大型语言模型（LLMs）内容安全对于其在现实应用中的部署至关重要。然而，现有安全防护措施主要针对高资源语言，导致大量使用低资源语言的人群未被覆盖。为此，我们引入CREST（CRoss-lingual Efficient Safety Transfer），一种参数高效多语言安全分类模型，仅需0.5B参数即可支持100种语言。通过在13种高资源语言的策略性子集上训练，模型利用基于聚类的跨语言迁移，从少数语言扩展到100种语言，实现了对未见过的高资源和低资源语言的有效泛化。该方法解决了低资源环境下训练数据有限的挑战。我们对六个安全基准进行了全面评估，证明CREST在与之相当的规模下优于现有最先进的防护措施，并在参数量达2.5B及以上的模型中取得竞争性结果。我们的发现突显了语言特定防护的局限性，并强调了开发能够有效扩展以服务全球人口的通用、语言无关的安全系统的重要性。
英文摘要：
Ensuring content safety in large language models (LLMs) is essential for their deployment in real-world applications. However, existing safety guardrails are predominantly tailored for high-resource languages, leaving a significant portion of the world's population underrepresented who communicate in low-resource languages. To address this, we introduce CREST (CRoss-lingual Efficient Safety Transfer), a parameter-efficient multilingual safety classification model that supports 100 languages with only 0.5B parameters. By training on a strategically chosen subset of only 13 high-resource languages, our model utilizes cluster-based cross-lingual transfer from a few to 100 languages, enabling effective generalization to both unseen high-resource and low-resource languages. This approach addresses the challenge of limited training data in low-resource settings. We conduct comprehensive evaluations across six safety benchmarks to demonstrate that CREST outperforms existing state-of-the-art guardrails of comparable scale and achieves competitive results against models with significantly larger parameter counts (2.5B parameters and above). Our findings highlight the limitations of language-specific guardrails and underscore the importance of developing universal, language-agnostic safety systems that can scale effectively to serve global populations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
