Update README.md

README.md

@@ -7,15 +7,15 @@ language:
 pretty_name: CaT-Bench
 ---
 
-# 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:** 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
@@ -23,7 +23,7 @@ CaT-BENCH (Causal and Temporal Benchmark) is aimed at assessing the ability of l
 - **Language(s) (NLP):** English
 - **License:** Apache License 2.0
 
-### Dataset Sources [optional]
+### Dataset Sources
 
 - **Repository:** [More Information Needed]
 - **Paper:** [Link to the arXiv paper when available]
@@ -32,7 +32,7 @@ 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
 
@@ -52,7 +52,7 @@ The dataset was created to address the need for evaluating language models' unde
 
 #### Data Collection and Processing
 
-The dataset is based on the 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 by Yamakata et al. (2020), which contains 300 English cooking recipes annotated with substep procedure dependencies. From this corpus:
 
 - **Selection:** 57 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.
@@ -61,9 +61,7 @@ The dataset is based on the Recipe Flow Graph Corpus by Yamakata et al. (2020),
 
 #### Who are the source data producers?
 
-The source data originates from the Recipe Flow Graph Corpus created by Yamakata et al. (2020). The recipes were collected from publicly available sources, and the dependency annotations were provided by the original authors.
-
-### Annotations [optional]
+The source data originates from the English Recipe Flow Graph Corpus created by Yamakata et al. (2020). The recipes were collected from publicly available sources, and the dependency annotations were provided by the original authors.
 
 #### Annotation process
 
@@ -73,10 +71,6 @@ Annotations were derived from the existing dependency graphs in the Recipe Flow
 - **Guidelines:** Annotators were provided with standardized instructions and examples.
 - **Inter-Annotator Agreement:** Measured using weighted Fleiss Kappa, indicating high agreement among annotators.
 
-#### Who are the annotators?
-
-The initial dependency annotations were created by the authors of the Recipe Flow Graph Corpus. For human evaluation of model explanations, annotators were U.S.-based crowd workers from Amazon Mechanical Turk with high approval ratings.
-
 #### Personal and Sensitive Information
 
 The dataset does not contain any personal, sensitive, or private information. All data are based on publicly available cooking recipes and contain no personally identifiable information.
@@ -89,12 +83,14 @@ The dataset does not contain any personal, sensitive, or private information. Al
 
 ### Recommendations
 
-Users should be cautious when generalizing results from this dataset to other domains or languages. It's recommended to use CAT-BENCH in conjunction with other benchmarks to get a comprehensive evaluation of a model's reasoning capabilities. Researchers should also be aware of potential biases in language models and consider additional evaluations for fairness and robustness.
+Users should be cautious when generalizing results from this dataset to other domains or languages. It's recommended to use CaT-Bench in conjunction with other benchmarks to get a comprehensive evaluation of a model's reasoning capabilities. Researchers should also be aware of potential biases in language models and consider additional evaluations for fairness and robustness.
 
-<!-- ## Citation [optional]
+## Citation
 
 **BibTeX:**
 
+Please cite both this benchmark and the original English Recipe Flow Graph Corpus dataset.
+
 ```bibtex
 @misc{lal2024catbench,
       title={CaT-BENCH: Benchmarking Language Model Understanding of Causal and Temporal Dependencies in Plans}, 
@@ -104,4 +100,34 @@ Users should be cautious when generalizing results from this dataset to other do
       archivePrefix={arXiv},
       primaryClass={cs.CL}
 }
-``` -->
+
+@inproceedings{yamakata-etal-2020-english,
+    title = "{E}nglish Recipe Flow Graph Corpus",
+    author = "Yamakata, Yoko  and
+      Mori, Shinsuke  and
+      Carroll, John",
+    editor = "Calzolari, Nicoletta  and
+      B{\'e}chet, Fr{\'e}d{\'e}ric  and
+      Blache, Philippe  and
+      Choukri, Khalid  and
+      Cieri, Christopher  and
+      Declerck, Thierry  and
+      Goggi, Sara  and
+      Isahara, Hitoshi  and
+      Maegaard, Bente  and
+      Mariani, Joseph  and
+      Mazo, H{\'e}l{\`e}ne  and
+      Moreno, Asuncion  and
+      Odijk, Jan  and
+      Piperidis, Stelios",
+    booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
+    month = may,
+    year = "2020",
+    address = "Marseille, France",
+    publisher = "European Language Resources Association",
+    url = "https://aclanthology.org/2020.lrec-1.638",
+    pages = "5187--5194",
+    language = "English",
+    ISBN = "979-10-95546-34-4",
+}
+```