π Website β’ π Paper β’ π» GitHub β’ ποΈ bird-interact-lite β’ ποΈ bird-interact-full β’ ποΈ LiveSQLBench
Mini-Interact is a lightweight version of BIRD-INTERACT (re-imagines Text-to-SQL evaluation via lens of dynamic interactions), which facilitates the quick development of interactive text-to-SQL methods. BIRD-INTERACT is built on top of single-turn unambiguous T2S tasks from LiveSQLBench.
Other Features same as BIRD-INTERACT: The evaluation is interactive, same as BIRD-INTERACT, where the model can interact with the user simulator or database to solve the task. Support two evaluation modes: (1) Conversational Interaction and (2) Agentic Interaction.
Status: β Currently Available
In this version, the system navigates uncertainty and ambiguity stemming from:
This release focuses on how AI systems handle ambiguity when knowledge is partial or imprecise.
Status: π Coming Soon
The upcoming personalized ambiguity version will tackle a different challenge:
| Feature | Status |
|---|---|
| π SELECT Queries | β Released |
| β CRUD Operations | π Coming Soon |
| π§ Knowledge-Based Ambiguities | β Released |
| π« Personalized Ambiguities | π Coming Soon |
| π¬ Follow-Up Questions | π Coming Soon |
git clone https://huggingface.co/datasets/birdsql/mini-interact
βοΈNOTE: If you find that some sqlite databases are not working, you could also download the database metafiles from the Google Drive.
To avoid data leakage by auto-crawling, we do not include GT solution sqls and test cases along with data in mini_interact.jsonl.
please email [email protected] with the tag [mini-interact GT&Test Cases] in title for full set, which will be sent automatically within 30 minutes.
Then refer to Combine the Public Data with the Ground Truth and Test Cases Section in our Github Repo to integrate the ground truth fields into the public data.
Refer to bird-interact repo for details of Evaluation.
data: Each data instance contain the following main parts:
instance_id: The unique identifier for the task.selected_database: The name of the database.amb_user_query: The user query with injected ambiguities.user_query_ambiguity: The ambiguities injected into the user query.non_critical_ambiguity: The non-critical ambiguities like order, limit, etc.knowledge_ambiguity: The ambiguities created by masked external knowledges.sol_sql: The ground truth SQL solution.preprocess_sql: SQL queries to run before executing the solution or prediction.clean_up_sql: SQL queries to run after the test cases to revert any changes made to the database.test_cases: A set of test cases to validate the predicted corrected SQL.external_knowledge: The external knowledge related to the specific task.Unambiguous User Query: Mini-Interact contains the transformed data from both bird-interact-lite and bird-interact-full. And, bird-interact-lite is built on top of single-turn unambiguous T2S tasks from LiveSQLBench-Base-Lite, and bird-interact-full is built on top of single-turn unambiguous T2S tasks from LiveSQLBench-Base-Full-v1. So, if you want to get the unambiguous user query, you could find it in the query field in LiveSQLBench-Base-Lite or LiveSQLBench-Base-Full-v1 by matching instance_id.
BIRD Team & Google Cloud
π Website β’ π Paper β’ π» GitHub β’ ποΈ bird-interact-lite β’ ποΈ bird-interact-full β’ ποΈ LiveSQLBench
Mini-Interact is a lightweight version of BIRD-INTERACT (re-imagines Text-to-SQL evaluation via lens of dynamic interactions), which facilitates the quick development of interactive text-to-SQL methods. BIRD-INTERACT is built on top of single-turn unambiguous T2S tasks from LiveSQLBench.
Other Features same as BIRD-INTERACT: The evaluation is interactive, same as BIRD-INTERACT, where the model can interact with the user simulator or database to solve the task. Support two evaluation modes: (1) Conversational Interaction and (2) Agentic Interaction.
Status: β Currently Available
In this version, the system navigates uncertainty and ambiguity stemming from:
This release focuses on how AI systems handle ambiguity when knowledge is partial or imprecise.
Status: π Coming Soon
The upcoming personalized ambiguity version will tackle a different challenge:
| Feature | Status |
|---|---|
| π SELECT Queries | β Released |
| β CRUD Operations | π Coming Soon |
| π§ Knowledge-Based Ambiguities | β Released |
| π« Personalized Ambiguities | π Coming Soon |
| π¬ Follow-Up Questions | π Coming Soon |
git clone https://huggingface.co/datasets/birdsql/mini-interact
βοΈNOTE: If you find that some sqlite databases are not working, you could also download the database metafiles from the Google Drive.
To avoid data leakage by auto-crawling, we do not include GT solution sqls and test cases along with data in mini_interact.jsonl.
please email [email protected] with the tag [mini-interact GT&Test Cases] in title for full set, which will be sent automatically within 30 minutes.
Then refer to Combine the Public Data with the Ground Truth and Test Cases Section in our Github Repo to integrate the ground truth fields into the public data.
Refer to bird-interact repo for details of Evaluation.
data: Each data instance contain the following main parts:
instance_id: The unique identifier for the task.selected_database: The name of the database.amb_user_query: The user query with injected ambiguities.user_query_ambiguity: The ambiguities injected into the user query.non_critical_ambiguity: The non-critical ambiguities like order, limit, etc.knowledge_ambiguity: The ambiguities created by masked external knowledges.sol_sql: The ground truth SQL solution.preprocess_sql: SQL queries to run before executing the solution or prediction.clean_up_sql: SQL queries to run after the test cases to revert any changes made to the database.test_cases: A set of test cases to validate the predicted corrected SQL.external_knowledge: The external knowledge related to the specific task.Unambiguous User Query: Mini-Interact contains the transformed data from both bird-interact-lite and bird-interact-full. And, bird-interact-lite is built on top of single-turn unambiguous T2S tasks from LiveSQLBench-Base-Lite, and bird-interact-full is built on top of single-turn unambiguous T2S tasks from LiveSQLBench-Base-Full-v1. So, if you want to get the unambiguous user query, you could find it in the query field in LiveSQLBench-Base-Lite or LiveSQLBench-Base-Full-v1 by matching instance_id.
BIRD Team & Google Cloud