SafeWorld: Geo-Diverse Safety Alignment (2412.06483v1)

Abstract: In the rapidly evolving field of 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.

• The paper introduces SAFE WORLD, a benchmark assessing LLM safety across geo-diverse cultural and legal contexts using 2,342 queries from 50 countries.
• It employs a multi-dimensional evaluation addressing contextual appropriateness, accuracy, and comprehensiveness, uncovering gaps in models like GPT-4-turbo.
• Direct Preference Optimization training enhanced safety alignment, with the SAFE WORLD LM showing nearly 20% higher performance in human evaluations from nine countries.

SAFE WORLD: Geo-Diverse Safety Alignment

The focal point of the research presented by Yin et al. is the introduction and implementation of a groundbreaking benchmark tailored for assessing the safety alignment of LLMs across various global contexts—titled SAFE WORLD. This paper highlights the often-overlooked aspect of geo-diversity, emphasizing the need for LLMs to account for diverse cultural and legal standards that vary widely between nations and regions. Disregarding these considerations can lead to conflicts and legal risks, as what is deemed permissible or polite in one locale may be unacceptable elsewhere.

Key Contributions and Findings

To achieve its objectives, the researchers constructed SAFE WORLD, incorporating a comprehensive set of 2,342 user queries verified for alignment with cultural and legal norms from 50 countries and 493 regions/races. This benchmark is distinct in its focus on assessing diverse safety concerns through a multi-dimensional automatic evaluation framework focusing on three core criteria: contextual appropriateness, accuracy, and comprehensiveness of LLM responses.

Upon evaluation, the results indicated a noticeable struggle among current LLMs, including advanced proprietary models like GPT-4-turbo, to satisfactorily meet the established criteria for culturally and legally sensitive contexts. Interestingly, despite GPT-4-turbo’s extensive parametric knowledge, which informed the creation of the test queries, it displayed limitations in recognizing and adequately responding to these geo-diverse safety guidelines.

Direct Preference Optimization (DPO) Alignment Training

The authors employed Direct Preference Optimization (DPO) to enhance the alignment of LLMs with geo-diverse safety standards. Through this method, they synthesized preference pairs to fine-tune LLM responses, helping models behave appropriately and reference relevant cultural-legal guidelines accurately. The results of this training were positive: SAFE WORLD LM, the model trained using this methodology, showed significant improvements, outperforming notable counterparts like GPT-4o by substantial margins in all evaluation dimensions. Human evaluations from nine different countries further corroborated these outcomes, noting nearly a 20% higher winning rate in helpfully and harmfully assessing responses.

Implications and Future Directions

This paper sets a precedent for recognizing the importance of geo-diversity and systematically incorporating it into the evaluation and training processes of LLMs. By addressing cultural and legal nuances, AI applications can improve their global applicability and reliability, thus minimizing the risk of unintentional offenses or legal repercussions. Given the findings, future research could explore expanding this benchmark to include a broader array of countries and delve into more nuanced scenarios. Additionally, integrating these methods with other advanced alignment techniques might further refine LLM performance across diverse cultural and legal landscapes.

In conclusion, the SAFE WORLD benchmark and associated alignment methods represent a meaningful step towards more universally responsible AI applications, emphasizing the need for linguistic models to be attuned to the intricate tape of global cultures and jurisdictions. This work not only contributes to the technical enhancement of LLMs but also underscores the importance of considering diverse human aspects in technological evolution.