formatting

Author: msakande

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

@@ -36,13 +36,15 @@ Model benchmarks assess LLMs and SLMs across the following categories: quality,
 
 Azure AI assesses the quality of LLMs and SLMs across various metrics that are grouped into two main categories: accuracy, and prompt assisted metrics:
 
-**Accuracy**
+
+- Accuracy
 
 | Metric | Description |
 |--------|-------------|
 | Accuracy | Accuracy scores are available at the dataset and the model levels. At the dataset level, the score is the average value of an accuracy metric computed over all examples in the dataset. The accuracy metric used is `exact-match` in all cases, except for the _HumanEval_ dataset that uses a `pass@1` metric. Exact match compares model generated text with the correct answer according to the dataset, reporting one if the generated text matches the answer exactly and zero otherwise. The `pass@1` metric measures the proportion of model solutions that pass a set of unit tests in a code generation task. At the model level, the accuracy score is the average of the dataset-level accuracies for each model. |
 
-**Prompt assisted metrics**
+
+- Prompt assisted metrics
 
 | Metric | Description |
 |--------|-------------|