Title: Benchmark of Benchmarks: Unpacking Influence and Code Repository Quality in LLM Safety Benchmarks
Primary area: datasets and benchmarks
Keywords: Meta-Study, Benchmark, Large Language Model, Safety

Abstract: The rapid growth of research in LLM safety makes it hard to track all advances. 
Benchmarks are therefore crucial for capturing key trends and enabling systematic comparisons. 
Yet, it remains unclear why certain benchmarks gain prominence, and no systematic assessment has been conducted on their academic influence or code quality.
This paper fills this gap by presenting the first multi-dimensional evaluation of the influence (based on five metrics) and code quality (based on both automated and human assessment) on LLM safety benchmarks, analyzing 31 benchmarks and 382 non-benchmarks across prompt injection, jailbreak, and hallucination.
Our findings challenge common assumptions: counterintuitively, we find that benchmark papers show no significant advantage in academic influence (e.g., citation count and density) over non-benchmark papers. 
We uncover a key misalignment: while author prominence correlates with paper influence, neither author prominence nor paper influence shows a significant correlation with code quality.
Our results also indicate substantial room for improvement in code and supplementary materials: only 39\% of repositories are ready‑to‑use, 16\% include flawless installation guides, and a mere 6\% address ethical considerations.
Given that the work of prominent researchers tends to attract greater attention, they need to lead the effort in setting higher standards.