lyrain2001/Auto-Fill-Benchmark

Auto-Fill Benchmark

Benchmark for predicting missing cell values in real-world tables, introduced in Auto-Fill: Learning to Predict Missing Values Accurately with Specialist Language Models (PVLDB 19(11), 2026 — arXiv:2607.19847). Each case is a real table in which exactly one cell is replaced by [MISSING], together with the ground-truth value.

• Code: https://github.com/lyrain2001/auto-fill
• Models: Auto-Fill-Qwen3-8B-Knowledge · Auto-Fill-Qwen3-8B-Reasoning · Auto-Fill-Qwen3-8B-Coding

What is included

9 of the paper's 11 benchmark datasets, 200 cases each (1,800 cases in total):

Dataset	Split	Source
Pub-XLS	ID	Relational tables parsed from .xlsx spreadsheets crawled from a search-engine index
Pub-BI	ID	Relational tables extracted from public business-intelligence (BI) models
Pub-Wiki	ID	Tables from a recent Wikipedia snapshot
Gov-CSV	ID	CSV files crawled from nationalarchives.gov.uk
Git-Parquet	ID	Parquet files crawled from GitHub
Pub-Web	OOD	General web tables extracted from public HTML pages
Rel-AR	OOD	Tables with column-level arithmetic relationships (e.g. TOTAL = PRICE * QUANTITY)
Rel-FD	OOD	Tables with real functional dependencies (e.g. ProductKey → ProductName)
Rel-ST	OOD	Tables with string-based relationships (e.g. full name = first name + last name)

ID / OOD = in-distribution / out-of-distribution with respect to the table sources used to train the Auto-Fill specialists (test tables are disjoint from training tables). For the Rel-* datasets the relationship is not given to the model; it has to be inferred from the table.

Not released: Ent-CSV and Ent-XLS

The paper's two remaining benchmarks — Ent-CSV (200 CSV files from a large enterprise's data lake) and Ent-XLS (200 enterprise .xlsx spreadsheets) — are proprietary enterprise data behind a corporate firewall and cannot be released. Results on these two datasets therefore cannot be reproduced from this release; all other numbers in the paper can.

Layout

sample200/
  <Dataset>/
    case_<i>/
      data.csv              # the table; exactly one cell is "[MISSING]"
      info.json             # position of the masked cell + ground truth
      ground_truth.csv      # Rel-AR, Rel-FD only: annotated column relationships (see below)
      ground_truth_sem.csv  # Rel-AR only
index.jsonl                 # one row per case (drives the dataset viewer)

info.json, e.g.

{"row_idx": 5, "col_idx": 0, "col_name": "FID", "label": "Parks.fid--c59f932_14cc29bd18f_-dfc", "output": "{\"value\": \"Parks.fid--c59f932_14cc29bd18f_-dfc\"}"}

row_idx / col_idx are 0-based (the row index does not count the header); label is the ground-truth value as a string; output (where present) is the same value wrapped as JSON.

Supplementary relationship annotations (not used by the evaluation code):

• Rel-AR/ground_truth_sem.csv — formula in column-header names (e.g. TOTAL=PRICE*QUANTITY); ground_truth.csv is the same with Excel column letters (A = first column). sample_type P/N = relationship holds / has violations.
• Rel-FD/ground_truth.csv — left_col,right_col,sample_type: candidate functional dependencies left_col → right_col (P = holds, N = violated).
• Rel-ST — the relationship annotations refer to columns of an auxiliary joined table that is not part of data.csv, so they are not included.

index.jsonl fields: dataset, case, distribution, row_idx, col_idx, col_name, label, n_rows, n_cols, table_csv.

Usage

Download with the layout the code expects:

hf download lyrain2001/Auto-Fill-Benchmark --repo-type dataset --local-dir Auto-Fill-Benchmark

Run one specialist on one dataset and evaluate (from the code repository):

python inference/run_benchmark.py --mode knowledge \
    --model_path lyrain2001/Auto-Fill-Qwen3-8B-Knowledge \
    --dataset Gov-CSV --benchmark Auto-Fill-Benchmark/sample200 --gpu_ids 0
python evaluation/evaluate_single_specialist.py \
    --results_dir results/benchmark/benchmark_knowledge --benchmark Auto-Fill-Benchmark/sample200

Or load the index with 🤗 Datasets:

from datasets import load_dataset
ds = load_dataset("lyrain2001/Auto-Fill-Benchmark", split="test")

Evaluation

A prediction counts as correct if it matches label under autofill.utils.is_correct (normalized string comparison with numeric tolerance and date/time parsing). Because models may abstain, the primary metric is Recall@Precision=0.9 (R@P90) — the fraction of cells filled correctly when a method only answers where its confidence is high enough to keep precision ≥ 0.9 — together with pAUPRC.

Auto-Fill with the three Qwen3-8B specialists and the calibrated ensemble (R@P90 / pAUPRC, from the paper):

Pub-XLS	Pub-BI	Pub-Wiki	Gov-CSV	Git-Parquet	Ent-CSV*	Ent-XLS*	Pub-Web	Rel-AR	Rel-FD	Rel-ST	Mean
0.53 / 0.48	0.62 / 0.61	0.29 / 0.28	0.50 / 0.49	0.59 / 0.58	0.59 / 0.56	0.66 / 0.64	0.28 / 0.23	0.99 / 0.99	0.89 / 0.89	1.00 / 0.99	0.63 / 0.61

* not released (see above).

License

The annotations in this release (masked cells, labels, relationship files, index.jsonl) are provided under CC BY 4.0. The tables themselves come from public sources (Wikipedia, nationalarchives.gov.uk, public GitHub repositories, public web pages, public BI models and spreadsheets) and remain subject to the terms of their original sources. Intended for research use.

Citation

@article{liu2026autofill,
  title={Auto-Fill: Learning to Predict Missing Values Accurately with Specialist Language Models},
  author={Liu, Yurong and He, Yeye and Dong, Haoyu and Xing, Junjie and Han, Shi and Zhang, Dongmei and Chaudhuri, Surajit},
  journal={Proceedings of the VLDB Endowment},
  volume={19},
  number={11},
  pages={3160--3173},
  year={2026}
}