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finer-sql-train-joint-fixed — the FINER-SQL joint BIRD+Spider training corpus with its reference SQLs re-attached to the right sample

18,087 training rows (9,428 BIRD train + 8,659 Spider train) over 215 training databases. Same rows, same order, same prompts as data/train/grpo_sql_writer_origdata_joint_oldprompt_full — only the reference SQLs in groundtruth_sqls[1:] differ.

Trainer exposure

grpo_writer.py:1264 binds the list. The binary execution match uses entry 0 only (grpo_writer.py:1346), but the atomic-ops shaping bonus takes the maximum over the whole list (grpo_writer.py:1410 and :1448 → atomic_ops/reward.py:40 score_against_list, "max Jaccard similarity between pred_sql and any SQL in gt_sqls"). So a reference that answers a different question paid a shaping bonus to a prediction that answered that other question.

The upstream bug, fixed

/home/datht/finer-sql/evaluation/add_correct_sqls_from_eval.py wrote an evaluation's correct SQLs onto the sample document addressed by {"db_id": db_id, "_id": sample_id} (old lines 144 and 210), assuming the collection's primary key _id is the same number as the evaluation's sample_id. Where the two id spaces disagree, that selects a different sample of the same database and the correct SQLs of sample N land on sample N−k. The corpus shows exactly that signature: of the 34,205 references that are textually closer to one other sample of their own database, 90.1% name a LATER sample and 73.9% sit at their own database's single dominant offset (bird/airline +5 throughout, works_cycles +5, public_review_platform +3, chicago_crime +9). The script now resolves the document by its own sample_id and refuses the write unless the document's own gold is the gold those SQLs were judged against.

Why this exists

A row's groundtruth_sqls is [gold] + references. Entry 0 is the benchmark gold; entries 1.. are FINER-SQL's own training-SQL enrichment. The trainer reads the whole list: the binary execution match uses entry 0 only, but the atomic-ops shaping bonus (atomic_ops/reward.py: score_against_list) takes the maximum similarity over every entry.

In the original corpus a block of references belongs to a different sample of the same database — a positive shift of one to ten sample ids, a later sample's references sitting at the head of an earlier sample's list. So the shaping bonus paid a prediction that answered a different question.

The example, verbatim from the original corpus (bird / airline, sample_id 5849, "Tell the number of flights that landed at Lake Charles Regional Airport on 2018/8/15"): references 1..11 were

SELECT SUM(CASE WHEN dep_delay <= 0 THEN 1 ELSE 0 END)
FROM airlines WHERE fl_date = '2018/8/1';

— eleven counts of on-time departures on 2018/8/1, which are sample_id 5854's, "How many flights departed on time on 8/1/2018?". In this corpus sample 5849 carries only Lake Charles counts and sample 5854 has its own references back.

How it was repaired

Every reference passes four gates against the gold it is attached to, or it leaves the list:

gate	question
i	it parses (sqlglot, SQLite dialect)
ii	it returns the gold's answer on the original training database — executed through the db_execution HTTP API at the trainer's own 60 s ceiling, compared with the campaign's canonical comparator
iii	no other gold of the same database is a better owner for it
iv	it is not the same statement as the gold, or as a reference the sample already keeps

Every execution record is fetched by the exact SQL (sha1(dataset\0db_id\0sql)), so a reference is only ever judged on its own result; the tables a reference reads are not a gate (the reward asks for the gold's answer, not its tables) and survive as the informational reads_extra_tables field in reference_audit.jsonl.

A removed reference is then offered back to the samples of its own database and re-attached to the one whose gold it passes every gate against — which is how the shifted blocks reach their real owner. Nothing is invented.

	references
before	239,338
after	200,137
of those, re-attached to a different sample	34,334

dataset	before	after
bird	132541	109429
spider	106797	90708

Why each of the 73,535 removed reference occurrences left its sample: answer differs from the gold on the original database 41,985; mis-attached, no sample accepts it 14,840; the same statement as the gold 7,584; the same statement as another reference of this sample 7,160; mis-attached, re-attached elsewhere 1,159; the reference does not execute 372; the gold does not execute 290; the gold timed out at 60 s 74; does not parse 64; the reference timed out at 60 s 7. The per-dataset table is in FIX_REPORT.md.

Files

file	what it is
`data-00000-of-00001.arrow`, `dataset_info.json`, `state.json`	the corpus, an HF `datasets` directory — load with `datasets.load_from_disk`
`FIX_REPORT.md`	root cause, mechanism, every count, the gates, and the sha256 of both arrow files
`removed_references.jsonl`	one line per removed reference: dataset, db_id, sample_id, its index in `groundtruth_sqls`, the SQL, the reason, and the samples it was re-attached to
`reference_audit.jsonl`	one line per reference occurrence of the input corpus, kept or not, with `reads_extra_tables`, `same_answer_as_gold` and `l1_shape`
`fix_summary.json`	the same counts, machine-readable

from huggingface_hub import snapshot_download
from datasets import load_from_disk
d = snapshot_download("thanhdath/finer-sql-train-joint-fixed", repo_type="dataset")
ds = load_from_disk(d)          # 18,087 rows

sha256 of data-00000-of-00001.arrow: 807b9e2660f29353bd1cf22aa6e8ecb13abeca3fcac2a443a0ef00e9f7e51961 (the corpus it was built from: 4475ac267d80b881d80ee3a65f44008ddbbd2df8606ab355ef099d0f53b6dfd3).

The (gold, neighbour) pair set built over the same corpus, with a provenance_check column marking every mis-attached reference, is at thanhdath/finer-sql-gold-neighbour-pairs.

Built by campaigns/chained_05b_tkde/pairset/fix_corpus.py.

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