AI Safety Benchmarks Analysis - The paper analyzes AI safety benchmarks, a novel approach as no prior work has examined the semantic extent covered by these benchmarks [13] - The research identifies six primary harm categories within AI safety benchmarks, noting varying coverage and breadth across different benchmarks [54] - Semantic coverage gaps exist across recent benchmarks and will evolve as the definition of harms changes [55] Methodology and Framework - The study introduces an optimized clustering configuration framework applicable to other benchmarks with similar topical use or LLM applications, demonstrating its scalability [55] - The framework involves appending benchmarks into a single dataset, cleaning data, and iteratively developing unsupervised learning clusters to identify harms [17][18] - The methodology uses embedding models (like MiniLM and MPNet), dimensionality reduction techniques (TSNE and UMAP), and distance metrics (Euclidean and Mahalanobis) to optimize cluster separation [18][19][41] Evaluation and Insights - Plotting semantic space offers a transparent evaluation approach, providing more actionable insights compared to traditional ROUGE and BLEU scores [56] - The research highlights the potential for bias in clusters and variations in prompt lengths across different benchmarks [51] - The study acknowledges limitations including methodological constraints, dimensionality reduction information loss, biases in embedding models, and the limited scope of analyzed benchmarks [51][52] Future Research Directions - Future research could explore harm benchmarks across diverse cultural contexts and investigate prompt-response relationships [53] - Applying the methodology to domain-specific datasets could further reveal differences and insights [53]