🌐 Website • 📄 Paper (ICLR 2026 Oral) • 💻 GitHub • 🗄️ bird-interact-lite • 🗄️ bird-interact-full • 🗄️ LiveSQLBench
BIRD-INTERACT, an interactive text-to-SQL benchmark, re-imagines Text-to-SQL evaluation via lens of dynamic interactions which is built on top of single-turn unambiguous T2S tasks from LiveSQLBench. The environment blends a hierarchical knowledge base, database documentation and a function-driven user simulator to recreate authentic enterprise environments across full CRUD operations. It offers two rigorous test modes: (1) passive Conversational Interaction and (2) active Agentic Interaction, spanning 600 annotated tasks including Business Intelligence (BI), CRUD operations and etc., each guarded by executable test cases. Typical evaluations trigger 1,968-5,496 interaction turns between model and user simulator, while state-of-the-art reasoning models currently solve only ≈24% and ≈18% of tasks, underscoring the benchmark's challenge.
BIRD-INTERACT supports two evaluation modes as mentioned above:
bird_interact_conv.bird_interact_agent.We have released a lite version of BIRD-INTERACT, bird-interact-lite, which includes 270 high-quality real-world tasks specifically for PostgreSQL. This is a good starting point for quick experimentation. This data is built on top of single-turn unambiguous T2S tasks from LiveSQLBench-Base-Lite.
Now, we are pleased to release the full version of BIRD-INTERACT, bird-interact-full, which includes 600 tasks for PostgreSQL. It covers more tasks, more databases, and databases contains N2M relationships and more noisy data. This data is built on top of single-turn unambiguous T2S tasks from LiveSQLBench-Base-Full-v1.
git clone https://huggingface.co/datasets/birdsql/bird-interact-full
To avoid data leakage by auto-crawling, we do not include GT solution sqls and test cases along with data in bird_interact_data.jsonl.
please email [email protected] with the tag [bird-interact-full GT&Test Cases] in title for full set, which will be sent automatically.
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 DB building, usage and evaluation.
Database: The complete PostgreSQL database and building scripts (init-databases_postgresql.sh) can be download from the Google Drive. The details of building the database can be referred to bird-interact repo.
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.follow_up: The labeled follow up questions.external_knowledge: The external knowledge related to the specific task.Unambiguous User Query: If you want to get the unambiguous user query, you could find it in the query field in LiveSQLBench-Base-Full-v1 by matching the instance_id.
evaluation: The evaluation code is available in the ./evaluation directory.
BIRD Team & Google Cloud
🌐 Website • 📄 Paper (ICLR 2026 Oral) • 💻 GitHub • 🗄️ bird-interact-lite • 🗄️ bird-interact-full • 🗄️ LiveSQLBench
BIRD-INTERACT, an interactive text-to-SQL benchmark, re-imagines Text-to-SQL evaluation via lens of dynamic interactions which is built on top of single-turn unambiguous T2S tasks from LiveSQLBench. The environment blends a hierarchical knowledge base, database documentation and a function-driven user simulator to recreate authentic enterprise environments across full CRUD operations. It offers two rigorous test modes: (1) passive Conversational Interaction and (2) active Agentic Interaction, spanning 600 annotated tasks including Business Intelligence (BI), CRUD operations and etc., each guarded by executable test cases. Typical evaluations trigger 1,968-5,496 interaction turns between model and user simulator, while state-of-the-art reasoning models currently solve only ≈24% and ≈18% of tasks, underscoring the benchmark's challenge.
BIRD-INTERACT supports two evaluation modes as mentioned above:
bird_interact_conv.bird_interact_agent.We have released a lite version of BIRD-INTERACT, bird-interact-lite, which includes 270 high-quality real-world tasks specifically for PostgreSQL. This is a good starting point for quick experimentation. This data is built on top of single-turn unambiguous T2S tasks from LiveSQLBench-Base-Lite.
Now, we are pleased to release the full version of BIRD-INTERACT, bird-interact-full, which includes 600 tasks for PostgreSQL. It covers more tasks, more databases, and databases contains N2M relationships and more noisy data. This data is built on top of single-turn unambiguous T2S tasks from LiveSQLBench-Base-Full-v1.
git clone https://huggingface.co/datasets/birdsql/bird-interact-full
To avoid data leakage by auto-crawling, we do not include GT solution sqls and test cases along with data in bird_interact_data.jsonl.
please email [email protected] with the tag [bird-interact-full GT&Test Cases] in title for full set, which will be sent automatically.
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 DB building, usage and evaluation.
Database: The complete PostgreSQL database and building scripts (init-databases_postgresql.sh) can be download from the Google Drive. The details of building the database can be referred to bird-interact repo.
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.follow_up: The labeled follow up questions.external_knowledge: The external knowledge related to the specific task.Unambiguous User Query: If you want to get the unambiguous user query, you could find it in the query field in LiveSQLBench-Base-Full-v1 by matching the instance_id.
evaluation: The evaluation code is available in the ./evaluation directory.
BIRD Team & Google Cloud