jq-bench — human slice

The first benchmark for natural-language-to-jq translation. 30 hand-curated tasks derived from real StackOverflow questions, with gold filters and gold outputs verified by executing real jq — no LLM-as-judge anywhere.

It is the canonical evaluation set of jq-coder-0.6B, but it is model-agnostic: any system that turns a natural-language request (+ a JSON sample) into a jq filter can be scored on it.

Why a human slice

Synthetic benchmarks generated by the same grammar that generated the training data measure in-distribution memorization, not competence. This slice is independent of any generation grammar by construction: every item comes from a real question asked by a real person, and questions that seeded the jq-coder training grammar (the top-voted tier) are explicitly excluded. The items favor constructs and compositions that synthetic grammars tend to miss: del, with_entries, compound to_entries, index, first/last, descending sort_by, regex test, if-has-else, update assignment |=, array subtraction, join, keys, recursive merge *, += on nested paths, group_by into a dynamic-key object, map_values, and hyphenated-key projection (.stuff["info-spec"]).

Format

One JSON object per line in humana.jsonl:

Field	Meaning
pedido_nl	The natural-language request (the question author's intent, tool mentions removed)
programa	Gold jq filter (accepted answer, adapted to a pure filter — no -r/-s/shell flags)
familia	humana/Q<question_id> — unique per item
idioma	Request language (en)
documentos	≥2 input JSON documents: the original from the question plus a variant with the same skeleton
saidas	Gold outputs, one per document, canonical jq -cS
origem	Provenance: StackOverflow question_id, url, titulo, autor, votos, licenca

Every gold output was computed by running the gold filter with real jq (1.8) and is re-validated automatically by the project's test suite — curation you can re-execute, not curation you have to trust.

Scoring

Run the candidate filter against every document of the item and diff the canonical output (jq -cS) against gold. Two published metrics:

• strict — byte-identical to gold on all documents;
• task-solved — additionally accepts equivalent output shapes (stream vs. array wrapper and similar convention differences).

An item only counts if all its documents pass — a filter that hardcodes values from the sample fails the variant document.

Baseline

Model	strict	task-solved
jq-coder-0.6B (v14, Q8_0)	10/30	11/30
jq-coder-0.6B (v13, Q8_0)	9/30	10/30

Yes, the human slice is hard — that is the point. Reference scores for frontier models are on the roadmap.

License and attribution

StackOverflow content is CC BY-SA 4.0; this dataset is redistributed under the same license. Each item carries per-item attribution in origem: the question URL, the author's display name, and the vote count at collection time. The gold programs are adapted from the accepted answers of the linked questions.

If you use jq-bench, please cite:

@misc{jqbench2026,
  title   = {jq-bench: an execution-verified benchmark for natural-language-to-jq translation},
  author  = {Edelmar Schneider},
  year    = {2026},
  url     = {https://huggingface.co/datasets/DominuZ/jq-bench}
}