Proxies for LLM training data: trace each source

Set the data contract before collection

Write down the source inventory, owner, permitted access path, collection window, document types, intended use, retention and deletion process, language scope, and accountable reviewer. Prefer an official feed, export, or licensed dataset when it fits the task. Have the appropriate policy, privacy, and legal owners review source-specific terms and use conditions. This workflow does not decide what any law permits.

Treat public visibility, fetch success, a license label, and permission for a particular model-training use as separate facts. The Robots Exclusion Protocol, Request for Comments (RFC) 9309 says its rules are not access authorization. A source can also set conditions beyond crawler instructions. A route change does not resolve those questions.

Preserve provenance from raw source to training row

The World Wide Web Consortium (W3C) provenance overview describes provenance as information about entities, activities, and agents involved in producing data. A small project manifest can apply that idea without claiming formal W3C conformance.

Keep the raw source reference separate from the normalized document ID. A hash helps identify exact bytes; it does not establish permission, accuracy, or document quality. Keep access and deletion controls for raw data separate from records that do not need the original content.

Prerequisites

Run the manifest example with Python 3.12.0, tested locally; it uses only the standard library.

Validate a manifest and exclude unresolved rows

This minimal local candidate-filter example uses synthetic records. It does not parse timestamps, verify hexadecimal digest format, or validate split names; add those checks before using it as a production admission gate. It checks that required provenance fields exist and that only records marked approved in the team's own review state can enter a training candidate list. The string approved is a workflow value, not a legal conclusion or proof that the evidence is sufficient.

from urllib.parse import urlparse

REQUIRED = {
    "document_id", "source_url", "retrieved_at", "source_version",
    "permission_status", "review_state", "review_evidence_url",
    "raw_sha256", "transform_version", "dedup_group", "split",
}
ALLOWED_STATUS = {"approved", "restricted", "unknown", "blocked"}
ROWS = [
    {
        "document_id": "doc-001",
        "source_url": "https://data.example.org/handbook",
        "retrieved_at": "2026-09-24T10:30:00Z",
        "source_version": "terms-2026-08",
        "permission_status": "approved",
        "review_state": "approved",
        "review_evidence_url": "https://data.example.org/terms",
        "raw_sha256": "a" * 64,
        "transform_version": "parser-3",
        "dedup_group": "group-001",
        "split": "train",
    },
    {
        "document_id": "doc-002",
        "source_url": "https://data.example.org/page",
        "retrieved_at": "2026-09-24T10:31:00Z",
        "source_version": "unknown",
        "permission_status": "unknown",
        "review_state": "pending",
        "review_evidence_url": "",
        "raw_sha256": "b" * 64,
        "transform_version": "parser-3",
        "dedup_group": "group-002",
        "split": "train",
    },
]

def validate(row):
    errors = [f"missing {key}" for key in sorted(REQUIRED - row.keys())]
    parsed = urlparse(row.get("source_url", ""))
    if parsed.scheme != "https" or not parsed.netloc:
        errors.append("source_url must be an absolute HTTPS URL")
    if row.get("permission_status") not in ALLOWED_STATUS:
        errors.append("permission_status is not a recognized workflow value")
    if row.get("review_state") == "approved" and not row.get("review_evidence_url"):
        errors.append("approved review requires an evidence reference")
    if len(row.get("raw_sha256", "")) != 64:
        errors.append("raw_sha256 must contain 64 characters")
    return errors

training_candidates = []
for row in ROWS:
    errors = validate(row)
    if not errors and row["permission_status"] == "approved" and row["review_state"] == "approved":
        training_candidates.append(row["document_id"])
    print(f'{row["document_id"]}: {"valid" if not errors else "; ".join(errors)}')
print(f"training candidates: {training_candidates}")

Expected result for the illustrative rows:

doc-001: valid
doc-002: valid
training candidates: ['doc-001']

The second row passes schema validation because unknown is a valid recorded state. It remains excluded from training_candidates. In a production pipeline, validate timestamps, source inventory membership, allowed split names, digest format, reviewer identity, and access to the evidence record. A schema check proves that fields are present and shaped as expected; it does not judge whether the review decision is correct.

Version filters and duplicate decisions

Record the parser and transformation versions, each filter reason, and the raw record from which a derived document came. Depending on the approved task, filters might remove unsupported formats, empty extraction, duplicate navigation, secrets, or personal data that the intended use does not require. A filter is a data-processing decision; it does not prove a corpus is safe or representative.

Use exact hashes for byte-identical content, then keep normalized-text or near-duplicate matches as reviewable candidates. Preserve every source row, the retained representative, rule version, and decision. Split data by duplicate group so detected copies do not appear on both sides of a held-out evaluation. Record what overlap check ran and what it could miss.

A publisher or internal owner may request removal or correction. Track the request through raw objects, derived rows, caches, exports, and future training jobs. Keep an audit event without retaining content that the approved retention policy requires removing.

Report corpus checks separately from model results

A corpus report can show rows by source, review state, language, filter reason, duplicate group, split, and missing field. Label these as corpus counts. A model evaluation needs its own held-out data, task definition, metric, baseline, and evaluation date. Corpus size or a successful fetch does not establish model quality.

A small hand-reviewed sample can expose parser failures before collection expands. Test empty extraction, duplicate URLs, missing source evidence, unresolved review state, and a deletion request. Re-run the acceptance checks after parser or dedup changes, and preserve the manifest version used to build each dataset artifact.

Keep training separate from retrieval

A retrieval-augmented generation (RAG) index is refreshed to answer requests from a changing document set. A training corpus is a versioned input to model training. The two workflows can share sources only when their access and reuse reviews allow it; the RAG index alone does not preserve the training manifest, filters, dedup policy, evaluation split, or deletion trace.

The Hugging Face dataset-card guide recommends documenting a dataset's contents, metadata, intended use, limitations, and potential bias. The Software Package Data Exchange (SPDX) license list provides identifiers and canonical references; an identifier helps identify a license but does not resolve source-specific conditions. Creative Commons describes the legal code as the text that contains the license terms. Route each source-specific decision to the responsible reviewer.

A proxy can provide a network route for a fetch that the project has approved. It cannot establish data ownership or permission, and it does not validate extraction or evaluation. For an approved collection that needs a proxy route, review ProxyLane plans and validate one source and one manifest record before scaling the job.

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