SafeWorld: Geo-Diverse Safety Alignment
Journal:
arXiv
Published Date:
Dec 9, 2024
Abstract
In the rapidly evolving field of Large Language Models (LLMs), ensuring
safety is a crucial and widely discussed topic. However, existing works often
overlook the geo-diversity of cultural and legal standards across the world. To
demonstrate the challenges posed by geo-diverse safety standards, we introduce
SafeWorld, a novel benchmark specifically designed to evaluate LLMs' ability to
generate responses that are not only helpful but also culturally sensitive and
legally compliant across diverse global contexts. SafeWorld encompasses 2,342
test user queries, each grounded in high-quality, human-verified cultural norms
and legal policies from 50 countries and 493 regions/races. On top of it, we
propose a multi-dimensional automatic safety evaluation framework that assesses
the contextual appropriateness, accuracy, and comprehensiveness of responses.
Our evaluations reveal that current LLMs struggle to meet these criteria. To
enhance LLMs' alignment with geo-diverse safety standards, we synthesize
helpful preference pairs for Direct Preference Optimization (DPO) alignment
training. The preference pair construction aims to encourage LLMs to behave
appropriately and provide precise references to relevant cultural norms and
policies when necessary. Our trained SafeWorldLM outperforms all competing
models, including GPT-4o on all three evaluation dimensions by a large margin.
Global human evaluators also note a nearly 20% higher winning rate in
helpfulness and harmfulness evaluation. Our code and data can be found here:
https://github.com/PlusLabNLP/SafeWorld.
