Update README.md

README.md

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 license: apache-2.0
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-# Dataset Card for 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. 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
 
 ### Dataset Description
 
-CAT-BENCH (Causal and Temporal Benchmark) is aimed at assessing the ability of language models to reason about causal and temporal relationships within natural language plans. The dataset is constructed from cooking recipes and contains 4,260 questions about causal dependencies spanning 57 unique plans. Each question asks whether a particular step in a recipe must occur before or after another step, challenging models to understand the underlying causal and temporal structure.
+CaT-BENCH (Causal and Temporal Benchmark) is aimed at assessing the ability of language models to reason about causal and temporal relationships within natural language plans. The dataset is constructed from cooking recipes and contains 4,260 questions about causal dependencies spanning 57 unique plans. Each question asks whether a particular step in a recipe must occur before or after another step, challenging models to understand the underlying causal and temporal structure.
 
 - **Curated by:** Yash Kumar Lal*, Vanya Cohen*, Nathanael Chambers, Niranjan Balasubramanian, Raymond Mooney (* equal contribution)
 - **Funded by [optional]:** DARPA KAIROS program under agreement number FA8750-19-2-1003, National Science Foundation under award IIS #2007290, DARPA's Perceptually-enabled Task Guidance (PTG) program under Contract No. HR001122C007
@@ -27,7 +27,7 @@ CAT-BENCH (Causal and Temporal Benchmark) is aimed at assessing the ability of l
 
 ### Direct Use
 
-CAT-BENCH is intended for evaluating and benchmarking the performance of language models on tasks requiring causal and temporal reasoning within natural language plans. Researchers and practitioners can use this dataset to assess how well models understand dependencies between steps in a plan, such as preconditions and effects, and improve model architectures or training methods accordingly.
+CaT-BENCH is intended for evaluating and benchmarking the performance of language models on tasks requiring causal and temporal reasoning within natural language plans. Researchers and practitioners can use this dataset to assess how well models understand dependencies between steps in a plan, such as preconditions and effects, and improve model architectures or training methods accordingly.
 
 ## Dataset Structure
 
@@ -41,7 +41,7 @@ The dataset consists of:
 
 ### Curation Rationale
 
-The dataset was created to address the need for evaluating language models' understanding of causal and temporal dependencies in natural language plans. While LLMs have shown impressive generative capabilities, their ability to comprehend and reason about the structure and dependencies within plans is less understood. CAT-BENCH aims to fill this gap by providing a benchmark specifically focused on this aspect.
+The dataset was created to address the need for evaluating language models' understanding of causal and temporal dependencies in natural language plans. While LLMs have shown impressive generative capabilities, their ability to comprehend and reason about the structure and dependencies within plans is less understood. CaT-BENCH aims to fill this gap by providing a benchmark specifically focused on this aspect.
 
 ### Source Data
 
@@ -92,7 +92,7 @@ Users should be cautious when generalizing results from this dataset to other do
 
 ```bibtex
 @misc{lal2024catbench,
-      title={CAT-BENCH: Benchmarking Language Model Understanding of Causal and Temporal Dependencies in Plans}, 
+      title={CaT-BENCH: Benchmarking Language Model Understanding of Causal and Temporal Dependencies in Plans}, 
       author={Yash Kumar Lal* and Vanya Cohen* and Nathanael Chambers and Niranjan Balasubramanian and Raymond Mooney},
       year={2024},
       eprint={arXiv:xxxx.xxxxx},