metadata
license: cc-by-4.0
language:
- en
tags:
- text-to-sql
- schema-linking
pretty_name: GRAST-SL evaluation sets
configs:
- config_name: spider_dev
data_files: evaluation_set/spider_dev.csv
- config_name: bird_dev
data_files: evaluation_set/bird_dev.csv
- config_name: spider2_lite_256
data_files: evaluation_set/spider2_lite_256.csv
- config_name: spider2_snow_256
data_files: evaluation_set/spider2_snow_256.csv
- config_name: spider_dev_case_by_case
data_files: comparison_with_prior_golds/spider_dev_case_by_case.parquet
- config_name: bird_dev_case_by_case
data_files: comparison_with_prior_golds/bird_dev_case_by_case.parquet
- config_name: spider_dev_label_errors
data_files: comparison_with_prior_golds/spider_dev_label_errors.parquet
- config_name: bird_dev_label_errors
data_files: comparison_with_prior_golds/bird_dev_label_errors.parquet
- config_name: summary_statistics
data_files: comparison_with_prior_golds/summary_statistics.parquet
GRAST-SL evaluation sets
Schema-linking evaluation sets of the paper "Scaling Text2SQL via LLM-efficient Schema Filtering with Functional Dependency Graph Rerankers".
Structure
evaluation_set/
spider_dev.csv 1,034 questions - question, gold SQL, gold columns
bird_dev.csv 1,534 questions
spider2_lite_256.csv 256 instances (Spider 2.0-Lite)
spider2_snow_256.csv 256 instances (Spider 2.0-Snow)
check_gold.py gold-error checker: every gold set non-empty and
every gold column present in the database schema
(all four sets pass with 0 errors)
schemas/ full schema list per database (db_id -> columns);
join on each row's db_id to get the schema of that
sample; what check_gold.py validates against
comparison_with_prior_golds/
{spider,bird}_dev_case_by_case.parquet per-question comparison with the prior
released gold labels (lexical matching);
error columns first
{spider,bird}_dev_label_errors.parquet one row per wrong gold column + reason
summary_statistics.parquet aggregate numbers
How the gold was built
One pipeline for all three benchmarks:
- Spider / BIRD: released gold SQL + schema → o4-mini extracts used columns → post-processing: gold ⊆ schema (0 invalid) → no empty gold set → parser cross-check → PK convention for star-only tables → per-instance audit.
- Spider 2.0 (Lite / Snow): shard-table grouping (
events_20201101, … →events_*) → same steps as above.
check_gold.py re-verifies gold ⊆ schema and non-emptiness from the shipped files. Prior
released Spider/BIRD golds used lexical name matching (a name is credited to every table
containing it) — the case-by-case CSVs list each resulting error.
Citation
@article{hoang2025scaling,
title={Scaling Text2SQL via LLM-efficient Schema Filtering with Functional Dependency Graph Rerankers},
author={Hoang, Thanh Dat and Nguyen, Thanh Tam and Huynh, Thanh Trung and Yin, Hongzhi and Nguyen, Quoc Viet Hung},
journal={arXiv preprint arXiv:2512.16083},
year={2025}
}