zzsi/synthetic-tax-forms

Synthetic Tax Forms

Synthetic Tax Forms is a public dataset for training and evaluating tax-form submission extraction models.

The dataset contains OCR-derived prompts, JSON schemas, extraction targets, and quality metadata for synthetic multi-form tax submissions. It is intended for SFT and RL experiments replacing proprietary ParseStep LLM extraction with open-source models.

Contents

• train.parquet: 2,599 training rows with a stable Hugging Face viewer-compatible schema.
• metadata.json: dataset generation metadata.

Row Types

• sonnet_parse_output: exact ParseStep distillation rows. The input is the production-style ParseStep prompt and the target is the Sonnet 4.5 ParseStep output.
• ground_truth: augmented prompt/schema rows. The input uses prompt and schema variants over the same OCR text and the target is synthetic ground truth.

Current Mix

• 199 exact Sonnet ParseStep trace rows.
• 2,400 augmented ground-truth rows.
• 4 schema styles: native, flattened_native, minimal_target, base_with_native_formsData.
• 3 prompt styles: parse_step_default, schema_system_user, concise_schema_user.

Schema Notes

messages is stored as structured chat messages: a list of {role, content} objects.

The following heterogeneous JSON columns are serialized as JSON strings for Parquet/Hugging Face viewer stability:

• target_json
• ground_truth_json
• teacher_parse_json
• schema
• metadata

Use json.loads(...) to recover those columns as objects.

Example:

import json
from datasets import load_dataset

ds = load_dataset("zzsi/synthetic-tax-forms", split="train")
row = ds[0]
messages = row["messages"]
target = json.loads(row["target_json"])
schema = json.loads(row["schema"])
metadata = json.loads(row["metadata"])

Suggested Filtering

For canonical extraction SFT/RL labels:

row["target_source"] == "ground_truth"

For exact Sonnet behavior distillation:

row["target_source"] == "sonnet_parse_output"

For a high-precision mixed SFT set:

row["target_source"] == "ground_truth" or not json.loads(row["metadata"])["trace_quality"]["issues"]

Important Caveats

This dataset is synthetic. Names, addresses, TINs, dates, and amounts are generated data and should not be treated as real taxpayer data.

Some exact teacher traces include quality warnings in metadata.trace_quality, such as teacher form-type mismatch versus ground truth. Consumers should filter or downweight exact teacher rows with non-empty metadata.trace_quality.issues when training purely from teacher outputs.

For most SFT/RL work, the ground_truth rows should be treated as canonical labels. The sonnet_parse_output rows are useful as behavior-distillation examples for matching the current ParseStep call pattern.

Public Export Sanitization

The public export removes local filesystem paths and raw internal LLM call payloads. Trace provenance is retained using package/trace file identifiers and quality metadata.