Update README.md

README.md

@@ -9,7 +9,7 @@ pretty_name: CaT-Bench
 
 # Dataset Card for CaT-Bench
 
-CaT-Bench is a benchmark dataset designed to evaluate large language models' (LLMs) understanding of causal and temporal dependencies in natural language plans, specifically in cooking recipes. It consists of questions that test whether one step must necessarily occur before or after another, requiring reasoning about preconditions, effects, and the overall structure of the plan.
+CaT-Bench is a benchmark dataset designed to evaluate large language models' (LLMs) understanding of causal and temporal dependencies in natural language plans, specifically in cooking recipes based on the English Recipe Flow Graph Corpus by Yamakata et al. (2020). It consists of questions that test whether one step must necessarily occur before or after another, requiring reasoning about preconditions, effects, and the overall structure of the plan.
 
 ## Dataset Details
 
@@ -38,7 +38,7 @@ CaT-Bench is intended for evaluating and benchmarking the performance of languag
 The dataset consists of:
 
 - **Plans:** 300 unique cooking recipes.
-- **Questions:** 4,260 binary (yes/no) questions about step dependencies.
+- **Questions:** 9,162 binary (yes/no) questions about step dependencies.
 - **Annotations:** Questions are labeled as dependent (steps are dependent) or non-dependent (steps are independent).
 
 ## Dataset Creation
@@ -51,9 +51,9 @@ The dataset was created to address the need for evaluating language models' unde
 
 #### Data Collection and Processing
 
-The dataset is based on the English Recipe Flow Graph Corpus by Yamakata et al. (2020), which contains 300 English cooking recipes annotated with substep procedure dependencies. From this corpus:
+The dataset is based on the English Recipe Flow Graph Corpus, which contains 300 English cooking recipes annotated with substep procedure dependencies. From this corpus:
 
-- **Selection:** 57 recipes were selected for inclusion.
+- **Selection:** 300 recipes were selected for inclusion.
 - **Question Generation:** For each ordered pair of steps, two binary questions were created regarding the necessity of one step occurring before or after another.
 - **Balancing:** The dataset was balanced to have an equal number of dependent and non-dependent questions.
 - **Annotations:** Steps were annotated based on whether there is a directed path between them in the recipe's dependency graph.