How to Build an AI Model Training Claim Ledger

How to Build an AI Training Claim Ledger

Build an AI training claim ledger by giving every material public statement a model version, source, evidence type, scope, uncertainty, allowed paraphrase, prohibited inference, reviewer, and refresh trigger.

The ledger stops a summary from becoming more certain than the paper.

flowchart LR
    A["Draft claim"] --> B["Classify evidence layer"]
    B --> C["Bind model, version, and source"]
    C --> D["Record method, scope, and uncertainty"]
    D --> E["Write allowed paraphrase"]
    E --> F["List prohibited inference"]
    F --> G["Review and publish gate"]
    G --> H["Refresh or retire"]

Create one row per claim

Do not create one citation row for an entire article.

Split statements when they contain separate facts. Model size, token count, data source, benchmark result, license, and deployment recommendation need different support.

Classify the evidence layer

Architecture evidence does not establish training data.

Training data evidence does not establish behavior.

A benchmark does not establish safety or deployment fit.

A license does not establish technical capability.

A repository listing does not establish that a deployed system uses the listed artifact.

Keep paper, model card, code, weights, license, policy, publisher benchmark, independent evaluation, and local system test distinct.

Bind the exact artifact

Record family, version, size, base or tuned form, tokenizer, prompt format, date, precision, conversion, and hash when relevant.

"Llama 3" can refer to several releases and artifacts.

The official Llama 3.1 model card, paper, repository, and license each support different rows.

Preserve publisher and independent status

Use "Meta reported" for Meta's training methods and benchmarks.

Use "an independent evaluation found" only when the evaluation is independent and the tested artifact and protocol match the sentence.

Use "our test found" only when the local record preserves the test.

Do not remove attribution because the result appears in a technical paper.

Record scope and missing evidence

A benchmark row should include dataset version, task, metric, shots, prompt, evaluator, decoding, comparison, and date.

A data row should include pretraining or post-training stage, disclosure type, token or document definition, source category, time cutoff, and missing corpus detail.

A scaling row should include paper, equation, variables, range, architecture, data regime, metric, uncertainty, and extrapolation.

A deployment row should include use case, artifact, hardware, runtime, prompts, retrieval, tools, safeguards, users, and consequence.

Missing is a valid ledger value.

Write the prohibited inference

This field catches most editorial drift.

If the source says "more than 15 trillion pretraining tokens," prohibited inferences include exact unique documents, exact words, total original crawl size, or exact cost.

If the source grants licensed use, prohibited inferences include unrestricted public-domain status or absence of obligations.

If a publisher reports a benchmark win, prohibited inferences include universal superiority or fitness for the reader's task.

If a guard model exists, prohibited inferences include a safe deployed system.

Use the ledger during writing

Link each material paragraph to claim IDs.

When the draft changes, compare it with the allowed paraphrase. If the sentence grows stronger, add evidence or narrow it.

Review headings, captions, metadata, descriptions, structured data, social copy, and image alt text. Overclaims often return outside the body.

Run a final unsupported-superlative and present-tense check.

Review the ledger as a system

Sort rows by source, reviewer, freshness, and missing evidence.

Look for one publisher page carrying too many different conclusions. Look for current-tense language supported only by a launch post. Find claims with no prohibited inference, because those rows often have not been challenged closely enough.

Check whether every page title and SEO description can be reconstructed from approved rows. Search snippets are claims too.

The ledger should make uncertainty visible without turning the article into a wall of disclaimers. Keep the evidence record internal, then write clean public prose that remains inside it.

When reviewers disagree, preserve the disagreement, decision, owner, and rationale. Do not silently choose the stronger marketing sentence.

Refresh and retire

Set triggers for new model versions, edited model cards, repository changes, license changes, benchmark revisions, independent studies, product retirement, and new local tests.

Preserve old rows as historical evidence. Add a successor or correction rather than overwriting what a source said at the time.

Archive the exact source or immutable identifier when permitted. Web pages and repositories can change, and a URL alone may not preserve the reviewed state.

Use [[How to Read Training Data Disclosures Without Inventing the Dataset]] for data rows. Use [[Llama 3.1 Technical Release Record]] as a worked model-family example.

This editorial method was developed with AI assistance from E029, official Llama sources, and the linked claim-ledger framework. Dalton Anderson remains the author. Editorial, technical, research, license, current-source, and founder review are mandatory before publication. Publication is not authorized.