Update README.md

README.md

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----
-license: cc-by-4.0
----
+---
+license: cc-by-sa-4.0
+task_categories:
+- table-question-answering
+- question-answering
+language:
+- en
+size_categories:
+- 1K<n<10K
+---
+
+# BIRD-SQL Dev
+
+## 🆕 Update 2025-11-06
+
+We would like to express our sincere gratitude to the community for their continuous support and constructive feedback on the **BIRD-SQL Dev** dataset. Over the past year, we have received valuable suggestions through GitHub discussions, emails, and user reports. Based on these insights, we organized a quality review program led by a team of five PhD researchers in Data Science and AI, supported by a globally distributed group of industry engineers with over 10 years of experience and master’s students in DS/AI. The team systematically reviewed all instances in BIRD Dev to minimize ambiguity and correct errors, ensuring improved clarity, consistency, and reliability throughout the dataset. Please note that all questions in BIRD SQL were written by experienced native speakers who received BI training in text-to-SQL annotation. While we have made significant efforts to minimize ambiguity, it remains an inherent feature of natural language and NLP research, reflecting the realistic challenges of interpreting human questions in database contexts. To further address this, we will follow the design of [BIRD-Interact](https://bird-interact.github.io/) and introduce an interactive, clarification-based setting as a new part of our leaderboard in the future, enabling models to handle ambiguity through dynamic interactions and clarification dialogues.
+
+### 🔍 What’s New
+This update focuses on improving the overall clarity, correctness, and consistency of the dataset.  
+In this release, we have:
+- Refined **questions** to remove ambiguity and improve natural-language clarity while preserving their original meaning.  
+- Revised **evidence** descriptions to make them concise, accurate, and properly scoped.  
+- Corrected **SQL queries** to ensure syntactic validity, logical consistency, and successful execution on all released databases.  
+
+---
+
+### ✅ TODOs
+
+- [ ] Release updated **Dev data**
+- [ ] Release **baseline results** on new Dev set
+- [ ] Integrate **interactive** setting into leaderboard
+
+
+
+### 📥 For New Users
+If you are new to **BIRD Dev**, you can download the complete databases using the following link:  
+[Download BIRD Dev Complete Package](https://drive.google.com/file/d/13VLWIwpw5E3d5DUkMvzw7hvHE67a4XkG/view?usp=sharing)
+Then you can pull the dataset from Hugging Face:
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("birdsql/bird_sql_dev_20251106")
+print(dataset[0])
+```
+
+### 🔄 For Existing Users
+If you have already downloaded the BIRD databases, you can pull the latest data updates through Hugging Face:
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("birdsql/bird_sql_dev_20251106")
+print(dataset[0])
+```
+
+## 🧱 Dataset Fields
+
+Each entry in **BIRD-SQL Dev** is a JSON object with the following structure:
+
+| Field | Type | Description |
+|:------|:-----|:-------------|
+| `question_id` | `int` | Unique identifier for each instance. |
+| `db_id` | `string` | Database name corresponding to a SQLite file. |
+| `question` | `string` | Natural-language question posed by the user. |
+| `evidence` | `string` or `null` | Supporting information or definitions needed to interpret the question. |
+| `SQL` | `string` | Ground-truth SQL query verified to execute successfully. |
+| `difficulty` | `string` | Difficulty level — one of `simple`, `moderate`, or `challenging`. |
+
+### Example
+```json
+{
+  "question_id": 0,
+  "db_id": "california_schools",
+  "question": "What is the highest eligible free rate for K-12 students in the schools in Alameda County?",
+  "evidence": "Eligible free rate for K-12 = `Free Meal Count (K-12)` / `Enrollment (K-12)`",
+  "SQL": "SELECT `Free Meal Count (K-12)` / `Enrollment (K-12)` FROM frpm WHERE `County Name` = 'Alameda' ORDER BY (CAST(`Free Meal Count (K-12)` AS REAL) / `Enrollment (K-12)`) DESC LIMIT 1",
+  "difficulty": "simple"
+}
+```
+
+
+## 🙌 Acknowledgement
+We sincerely thank the participating members for their time and dedication in improving this release: Benjamin Jun-jie Glover, Pan Enze, Rain Yiran Xu, Ashley (Juyeon) Lee, Eric Yue Wu, Yu Kaijia, Ziye Luo, Tangpirul Tat, Chik Ki Lok, Xu Haosen, Zhao Mingze, Chen Bingshang, Huang Yingrui, Winiera Sutanto, Zhan Mohan, Leia (Heaju) Kim, Veren Florecita, Xu Zixi, Chui Ting Yu George
+
+
+We also appreciate the continuous support and feedback from the open community, including GitHub reviewers (@element154, @NL2SQL-Empirical, @erikskalnes), anonymous users, and those who reached out to us via email, such as Arcwise AI (@hansonw), for their valuable suggestions.
+
+## 📝 Citation
+Please cite the repo if you think our work is helpful to you.
+```
+@article{li2024can,
+  title={Can llm already serve as a database interface? a big bench for large-scale database grounded text-to-sqls},
+  author={Li, Jinyang and Hui, Binyuan and Qu, Ge and Yang, Jiaxi and Li, Binhua and Li, Bowen and Wang, Bailin and Qin, Bowen and Geng, Ruiying and Huo, Nan and others},
+  journal={Advances in Neural Information Processing Systems},
+  volume={36},
+  year={2024}
+}
+```