Datasets:

ottema
/

structfix-bench

license: apache-2.0
task_categories:
  - text2text-generation
language:
  - en
tags:
  - json-repair
  - schema-validation
  - structured-output
  - tool-calling
  - agent-workflows
  - constraint-dsl
size_categories:
  - 100K<n<1M

StructFix-Bench

A benchmark for schema-aware structured output recovery — repairing broken outputs from agents, tool calls, and LLM workflows.

What it tests

Unlike JSON syntax repair benchmarks, StructFix-Bench focuses on semantic and schema-level recovery:

Replacing invalid enum values with valid ones
Adding missing required fields
Correcting type mismatches
Recovering tool call arguments from Python syntax
Reconstructing outputs from truncated agent chains
Extracting JSON from markdown/text wrappers

Dataset splits

Split	Examples	Field names	Enums
`train`	200,000	Mixed (semantic + shuffled)	Mixed (realistic + synthetic)
`validation`	20,000	Semantic	Realistic
`test_seen`	10,000	Semantic	Realistic
`test_unseen`	10,000	Semantic	Realistic
`test_random_fields`	10,000	Random hex (`f_8f31a7`)	Synthetic

Corruption types (28 total)

Syntactic (10)

single_quotes, unquoted_keys, trailing_comma, markdown_fences, extra_text_before, extra_text_after, truncated_object, truncated_array, missing_bracket, missing_brace

Semantic (9)

wrong_type, invalid_enum, missing_required, null_required, extra_field, boolean_as_string, number_with_unit, synonym_enum, date_format_error

LLM-like (3)

markdown_explanation, partial_structure, thought_process

Tool/Agent (6)

tool_call_bad_format, tool_call_text_mix, tool_call_python_syntax, tool_call_partial_args, tool_call_wrong_param, agent_chain

Input format

Each example uses ConstraintDSL to represent the schema:

TASK repair_structured_output

SPEC
FIELD priority TYPE string VALUES low|medium|high REQUIRED yes
FIELD description TYPE string REQUIRED yes

BROKEN_OUTPUT
{"priority":"urgent"}

Target: {"priority":"high","description":""}

Key results

Method	Schema Success (unseen)	Schema Success (random fields)
json-repair	65.2%	64.0%
CodeT5+ + JSON Schema	55.0%	—
CodeT5+ + ConstraintDSL	96.3%	91.9%

Schema ablation study

Full ablation results are included in results/all_results.json:

DSL variant	Schema Success
Full ConstraintDSL	96.3%
No DSL at all	78.4%
Dummy DSL	75.0%
Conflicting DSL	88.3%
Shuffled field names	73.4%
Shuffled enum values	89.8%
Shuffled required flags	93.2%
Minimal DSL	82.5%
Field names only	72.2%

Known limitations

The benchmark uses synthetically generated schemas and corruptions. A showcase validation with 25 hand-crafted real-world examples revealed that model performance degrades when field names overlap with training vocabulary — the model may substitute field names (e.g., action → active, contract_id → consign_id). See the model card for details.

Files

train.jsonl / validation.jsonl / test_seen.jsonl / test_unseen.jsonl / test_random_fields.jsonl
results/all_results.json — all experiment results including ablations

Each example contains

{
  "input": "TASK repair_structured_output\n\nSPEC\n...",
  "target": "{\"priority\":\"high\",\"description\":\"\"}",
  "corruption_type": "invalid_enum",
  "schema": {"type": "object", "properties": {...}, "required": [...]},
  "invalid_json": "{\"priority\":\"urgent\"}",
  "error": "Field 'priority' has invalid enum value 'urgent'",
  "target_json": "{\"priority\":\"high\",\"description\":\"\"}"
}

Citation

@software{structfix_bench,
  title = {StructFix-Bench: A Benchmark for Schema-Aware Structured Output Recovery},
  author = {Ottema},
  year = {2026},
  url = {https://huggingface.co/datasets/ottema/structfix-bench}
}

License

Apache-2.0