A Taxonomy of Safety: Harmonizing LLM Benchmarks in a Fragmented Landscape

Abstract

Understanding and mitigating the safety limitations of LLMs is of great importance to build trustworthy AI applications. Although a wide range of safety benchmarks are available, there is no standardized taxonomy of safety categories. As a result, some benchmarks focus on a specific subset of categories, they define test samples on different granularity levels, or they use different definitions or naming conventions. To mitigate these issues, we propose a two-level taxonomy of LLM safety categories, created by harmonizing existing resources. Our taxonomy gives an overview of important safety categories that helps researchers pinpoint potential safety risks and select the right benchmarks when evaluating or developing language models. Moreover, the taxonomy provides guidelines to categorize future benchmarks. Furthermore, since the majority of the available safety resources are English-focused, we check the cross-cultural validity of our taxonomy by translating datasets covering all top level categories to French, German, Italian, and Spanish. A manual review of a subset of translated samples by native speakers revealed no major cultural mismatches from a safety perspective. This supports not only the transferability of English benchmarks but also the transferability of the categories in our taxonomy, as well as its potential as a practical tool for guiding safety-focused dataset development and evaluation beyond English.

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