Update articles/ai-foundry/concepts/model-benchmarks.md

Co-authored-by: Lauryn Gayhardt <[email protected]>

Author: changliu2

articles/ai-foundry/concepts/model-benchmarks.md

@@ -91,7 +91,7 @@ These 7 categories can be summarized into 3 functional categories capturing stan
 [Toxigen](https://github.com/microsoft/TOXIGEN) is a large-scale machine-generated dataset for adversarial and implicit hate speech detection. It contains implicitly toxic and benign sentences mentioning 13 minority groups. We use the annotated samples from Toxigen for evaluation and calculate accuracy scores to measure performance. Higher accuracy is better, because scoring high on this dataset model is better ability to detect toxic content. Model benchmarking is performed with Azure AI Content Safety Filter turned off.
 
 ### Model knowledge in sensitive domains
-The [Weapons of Mass Destruction Proxy](https://github.com/centerforaisafety/wmdp) (WMDP) benchmark measures model knowledge of in sensitive domains including biosecurity, cybersecurity, and chemical security. The leaderboard uses average accuracy across cybersecurity, biosecurity, and chemical security. A higher WMDP accuracy score denotes more knowledge of dangerous capabilities (i.e., worse behaviour from a safety standpoint). Model benchmarking is performed with the default Azure AI Content Safety filters on. These safety filters detect and block content harm in violence, self-harm, sexual, hate and unfaireness, but do not target categories in cybersecurity, biosecurity, chemical security.
+The [Weapons of Mass Destruction Proxy](https://github.com/centerforaisafety/wmdp) (WMDP) benchmark measures model knowledge of in sensitive domains including biosecurity, cybersecurity, and chemical security. The leaderboard uses average accuracy across cybersecurity, biosecurity, and chemical security. A higher WMDP accuracy score denotes more knowledge of dangerous capabilities (worse behavior from a safety standpoint). Model benchmarking is performed with the default Azure AI Content Safety filters on. These safety filters detect and block content harm in violence, self-harm, sexual, hate and unfairness, but don't target categories in cybersecurity, biosecurity, chemical security.
 
 ### Limitations of safety benchmarks
 We understand and acknowledge that safety is a complex topic and has several dimensions. No single current open-source benchmarks can test or represent the full safety of a system in different scenarios. Additionally, most of these benchmarks suffer from saturation, or misalignment between benchmark design and the risk definition, can lack clear documentation on how the target risks are conceptualized and operationalized, making it difficult to assess whether the benchmark accurately captures the nuances of the risks. This limitation can lead to either overestimating or underestimating model performance in real-world safety scenarios.