Update README.md

README.md

@@ -1,3 +1,86 @@
 ---
 license: apache-2.0
+task_categories:
+- table-question-answering
+language:
+- en
+size_categories:
+- n<1K
 ---
+
+This repository contains the CRT-QA dataset, which includes question-answer pairs that require complex reasoning over tabular data. 🚀
+
+## About the Dataset and Paper
+
+- **Title:** CRT-QA: A Dataset of Complex Reasoning Question Answering over Tabular Data
+- **Conference:** EMNLP 2023
+- **Authors:** [Zhehao Zhang](https://zzh-sjtu.github.io/zhehaozhang.github.io/), Xitao Li, Yan Gao, Jian-Guang Lou 👩‍💼👨‍💼
+- **Affiliation:** Dartmouth College, Xi'an Jiaotong University, Microsoft Research Asia 🏢
+
+### Data Format
+
+The data is stored in a json file, structured with the following fields for each datapoint (keyed by a .csv file table):
+
+```
+Question name, Title, step1, step2, step3, step4, Answer, Directness, Composition Type
+```
+
+- `Question name`: The text of the question
+- `Title`: The title of the table that the question refers to
+- `step1` to `step4`: Steps describing the reasoning process and operations used to answer the question
+  - `type`: `Operation` or `Reasoning`
+  - `name`: Name of the specific operation or reasoning type
+  - `detail`: Additional details about the step
+- `Answer`: The answer text
+- `Directness`: `Explicit` or `Implicit` question
+- `Composition Type`: `Bridging`, `Intersection`, or `Comparison`
+- 
+### Reasoning and Operations
+
+The reasoning and operations referenced in the `step` fields come from a defined taxonomy:
+
+**Operations:**
+- Indexing
+- Filtering
+- Grouping
+- Sorting
+
+**Reasoning:**
+- Grounding
+- Auto-categorization
+- Temporal Reasoning
+- Geographical/Spatial Reasoning
+- Aggregating
+- Arithmetic
+- Reasoning with Quantifiers
+- Other Commonsense Reasoning
+
+### Contact 📧
+
+For inquiries or updates about this repository, please contact [zhehao.zhang.gr@dartmouth.edu]. 📬
+
+### Citation
+
+If you use this dataset in your research, please cite the following paper:
+
+```
+@inproceedings{zhang-etal-2023-crt,
+    title = "{CRT}-{QA}: A Dataset of Complex Reasoning Question Answering over Tabular Data",
+    author = "Zhang, Zhehao  and
+      Li, Xitao  and
+      Gao, Yan  and
+      Lou, Jian-Guang",
+    editor = "Bouamor, Houda  and
+      Pino, Juan  and
+      Bali, Kalika",
+    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
+    month = dec,
+    year = "2023",
+    address = "Singapore",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2023.emnlp-main.132",
+    doi = "10.18653/v1/2023.emnlp-main.132",
+    pages = "2131--2153",
+    abstract = "Large language models (LLMs) show powerful reasoning abilities on various text-based tasks. However, their reasoning capability on structured data such as tables has not been systematically explored. In this work, we first establish a comprehensive taxonomy of reasoning and operation types for tabular data analysis. Then, we construct a complex reasoning QA dataset over tabular data, named CRT-QA dataset (Complex Reasoning QA over Tabular data), with the following unique features: (1) it is the first Table QA dataset with multi-step operation and informal reasoning; (2) it contains fine-grained annotations on questions{'} directness, composition types of sub-questions, and human reasoning paths which can be used to conduct a thorough investigation on LLMs{'} reasoning ability; (3) it contains a collection of unanswerable and indeterminate questions that commonly arise in real-world situations. We further introduce an efficient and effective tool-augmented method, named ARC (Auto-exemplar-guided Reasoning with Code), to use external tools such as Pandas to solve table reasoning tasks without handcrafted demonstrations. The experiment results show that CRT-QA presents a strong challenge for baseline methods and ARC achieves the best result.",
+}
+```