About BIRD
BIRD (BIg Bench for LaRge-scale Database Grounded Text-to-SQL Evaluation) represents a pioneering, cross-domain dataset that examines the impact of extensive database contents on text-to-SQL parsing. BIRD contains over 12,751 unique question-SQL pairs, 95 big databases with a total size of 33.4 GB. It also covers more than 37 professional domains, such as blockchain, hockey, healthcare and education, etc.
News
- Mar 13, 2024: Please also take a look at our related work: Tapilot-Crossing, which is the first challenging and more realistic benchmark designed to evaluate Large Language Model (LLM) agents on interactive data analysis tasks. The code includes Python and Private Library. And it covers 6 common agent actions in evaluation.
- Sept 25, 2023: We have released a cleaner version of
dev set
. Please download dev set again. We checked all cases of dev set and fixed all errors that we found. After cleaning, the ChatGPT (gpt-3.5-turbo) and GPT4 (gpt-4-32k) EX scores have improved to 42.24 (from 37.22) and 49.15 (from 46.35), respectively. Thanks for all feedbacks! - Sept 21, 2023: Our paper has been accepted by
NeurIPS 2023
as aSpotlight
!!! Thanks for all the efforts and suggestions of co-authors, anonymous reviewers, awesome researchers/users in github or emails. - July 17, 2023: We update newest results of GPT-4, Claude-2 and Palm-2.
- July 14, 2023: The data link has been updated, fixing the schema names in the CSV files. Additionally, tied results caused by
order_by limit 1
are now considered. Both SQL queries - with and without accounting for tied results - are valid at this time. - Jun 12, 2023: We are welcome to any suggestions and reported gold errors in help_wanted. Any of your help is appreciated!
- Jun 5, 2023: We open-sourced our Graphix-T5, a graph-aware semi-pretrained text-to-text PLM specifically designed to improve multi-hop reasoning for the complex text-to-SQL task.
- May 30, 2023: If you are interested in ICL, please check out our interesting work deep-thinking🤔. Generate 1000 models for 1000 people smoothly!
Surprise from BIRD
1. Large and Dirty values: Due to the nature of the real-world scenarios from which BIRD's database values were collected, they typically retain their original and frequently "dirty" format. Hence, text-to-SQL parsers must first analyze these values to account for their non-standard format before engaging in reasoning.
2. External Knowledge: "account.type = 'OWNER'" can be inferred by the knowledge evidence: "The condition of the loans require the account type should be the owner."
3. Text-to-Efficient-SQL: BIRD is the first text-to-SQL benchmark designed to encourage semantic parsers to produce SQL queries that are not only correct but also efficient. This emphasis on efficiency is especially valuable in real-world data / business analysis circumstances.
Submission
Please follow the Submission Guideline (below) and contact bird.bench23@gmail.com
for test evaluation. Ususally, we will return your results in 10 days!
Subscribe to BIRD Update
Bird is a long-term research project aimed at bridging the gap between semantic parsing models and the success of database applications. To receive the latest updates of the dataset, you can leave your email address.
Citation
@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} }
Model | Code | Size | Oracle Knowledge | Dev (%) | Test (%) | |
---|---|---|---|---|---|---|
Human Performance Data Engineers + DB Students |
✔️ | 92.96 | ||||
Jan 14, 2024 | MCS-SQL + GPT-4 Dunamu |
UNK | ✔️ | 63.36 | 65.45 | |
Apr 08, 2024 | OpenSearch-SQL,v1 + GPT-4 Alibaba Cloud |
UNK | ✔️ | 61.34 | 64.95 | |
Feb 27, 2024 | PB-SQL, v1 Seoul National University |
UNK | ✔️ | 60.50 | 64.84 | |
Feb 21, 2024 |
Sense Anonymous |
13B | ✔️ | 55.48 | 63.39 | |
Apr 10, 2024 | GRA-SQL Tencent CDP-youpu |
UNK | ✔️ | 62.58 | 63.22 | |
Mar 27, 2024 | {Chat2Query} (GPT-4 + data entity modeling) (PingCAP) PingCAP |
[link] | UNK | ✔️ | 58.15 | 60.98 |
Nov 16, 2023 | Dubo-SQL, v1 Mercator Technologies |
UNK | ✔️ | 59.71 | 60.71 | |
Oct 12, 2023 | SFT CodeS-15B Renmin University of China [Li et al. SIGMOD'24] |
[link] | 15B | ✔️ | 58.47 | 60.37 |
Feb 27, 2024 | DTS-SQL + DeepSeek 7B University of Alberta [Pourreza et al. '24] |
[link] | 7B | ✔️ | 55.8 | 60.31 |
Nov 21, 2023 | MAC-SQL + GPT-4 BUAA & Tencent [Wang et al. '23] |
UNK | ✔️ | 57.56 | 59.59 | |
Oct 12, 2023 | SFT CodeS-7B Renmin University of China [Li et al. SIGMOD'24] |
[link] | 7B | ✔️ | 57.17 | 59.25 |
Nov 09, 2023 | DAIL-SQL + GPT-4 Alibaba Group [Gao and Wang et al. VLDB'24] |
[link] | UNK | ✔️ | 54.76 | 57.41 |
Aug 15, 2023 | DIN-SQL + GPT-4 University of Alberta [Pourreza et al. '23] |
[link] | UNK | ✔️ | 50.72 | 55.90 |
Jul 01, 2023 | GPT-4 Baseline |
[link] | UNK | ✔️ | 46.35 | 54.89 |
Jul 16, 2023 | Claude-2 Baseline |
[link] | UNK | ✔️ | 42.70 | 49.02 |
Nov 23, 2023 | Open-SQL Anonymous |
7B | ✔️ | 37.68 | 47.74 | |
Mar 17, 2023 | ChatGPT + CoT HKU & DAMO [Li et al. NeurIPS'23] |
[link] | UNK | ✔️ | 36.64 | 40.08 |
Mar 17, 2023 | ChatGPT Baseline |
UNK | ✔️ | 37.22 | 39.30 | |
Feb 17, 2023 | Codex Baseline |
175B | ✔️ | 34.35 | 36.47 | |
Jul 16, 2023 | Palm-2 Baseline |
[link] | UNK | ✔️ | 27.38 | 33.04 |
Mar 17, 2023 | ChatGPT + CoT HKU & DAMO [Li et al. NeurIPS'23] |
[link] | UNK | 25.88 | 28.95 | |
Mar 17, 2023 | ChatGPT Baseline |
UNK | 24.05 | 26.77 | ||
Feb 17, 2023 | Codex Baseline |
175B | 25.42 | 24.86 | ||
Feb 5, 2023 | T5-3B Baseline |
3B | ✔️ | 23.34 | 24.05 | |
Feb 3, 2023 | T5-Large Baseline |
770M | ✔️ | 19.75 | 20.94 | |
Feb 3, 2023 | T5-Base Baseline |
220M | ✔️ | 11.54 | 12.89 | |
Feb 5, 2023 | T5-3B Baseline |
3B | 10.37 | 11.17 | ||
Feb 3, 2023 | T5-Large Baseline |
770M | 9.71 | 10.38 | ||
Feb 3, 2023 | T5-Base Baseline |
220M | 6.32 | 7.06 |
Model | Code | Size | Oracle Knowledge | Dev | Test | |
---|---|---|---|---|---|---|
Human Performance Data Engineers + DB Students |
✔️ | 90.27 | ||||
Jan 14, 2024 | MCS-SQL + GPT-4 Dunamu |
UNK | ✔️ | 64.82 | 71.35 | |
Apr 10, 2024 | GRA-SQL Tencent CDP-youpu |
UNK | ✔️ | 67.55 | 69.56 | |
Feb 27, 2024 | PB-SQL Seoul National University |
UNK | ✔️ | 71.31 | 68.90 | |
Apr 08, 2024 | OpenSearch-SQL,v1 + GPT-4 Alibaba Cloud |
UNK | ✔️ | 68.38 | 68.80 | |
Nov 21, 2023 | MAC-SQL + GPT-4 BUAA & Tencent [Wang et al. '23] |
UNK | ✔️ | 58.76 | 67.68 | |
Feb 27, 2024 | DTS-SQL + DeepSeek 7B University of Alberta [Pourreza et al. '24] |
[link] | 7B | ✔️ | 60.31 | 64.52 |
Oct 12, 2023 | SFT CodeS-15B Renmin University of China [Li et al. SIGMOD'24] |
[link] | 15B | ✔️ | 59.87 | 64.22 | Mar 27, 2024 | {Chat2Query} (GPT-4 + data entity modeling) (PingCAP) PingCAP |
[link] | UNK | ✔️ | -- | 63.89 |
Oct 12, 2023 | SFT CodeS-7B Renmin University of China [Li et al. SIGMOD'24] |
[link] | 7B | ✔️ | 58.80 | 63.62 |
Nov 16, 2023 | Dubo-SQL, v1 Mercator Technologies |
UNK | ✔️ | 66.01 | 63.00 | |
Nov 09, 2023 | DAIL-SQL + GPT-4 Alibaba Group [Gao and Wang et al. VLDB'24] |
[link] | UNK | ✔️ | 56.08 | 61.95 |
Jul 01, 2023 | GPT-4 Baseline |
[link] | UNK | ✔️ | 49.77 | 60.77 |
Aug 15, 2023 | DIN-SQL + GPT-4 University of Alberta [Pourreza et al. '23] |
[link] | UNK | ✔️ | 58.79 | 59.44 |
Mar 17, 2023 | ChatGPT + CoT HKU & DAMO [Li et al. NeurIPS'23] |
[link] | UNK | ✔️ | 42.30 | 56.56 |
Mar 17, 2023 | ChatGPT Baseline |
UNK | ✔️ | 43.81 | 51.40 | |
Mar 17, 2023 | ChatGPT + CoT HKU & DAMO [Li et al. NeurIPS'23] |
[link] | UNK | 32.33 | 49.69 | |
Nov 23, 2023 | OPEN-SQL Anonymous |
7B | ✔️ | 41.56 | 48.08 | |
Feb 17, 2023 | Codex Baseline |
175B | ✔️ | 43.41 | 41.60 | |
Mar 17, 2023 | ChatGPT Baseline |
UNK | 27.97 | 36.68 | ||
Feb 17, 2023 | Codex Baseline |
175B | 33.37 | 35.40 | ||
Feb 5, 2023 | T5-3B Baseline |
3B | ✔️ | 25.57 | 27.80 | |
Feb 3, 2023 | T5-Large Baseline |
770M | ✔️ | 22.74 | 25.00 | |
Feb 5, 2023 | T5-3B Baseline |
3B | 13.62 | 15.17 | ||
Feb 3, 2023 | T5-Base Baseline |
220M | ✔️ | 12.90 | 14.70 | |
Feb 3, 2023 | T5-Large Baseline |
770M | 9.90 | 12.25 | ||
Feb 3, 2023 | T5-Base Baseline |
220M | 7.78 | 8.97 |