Section 114 · Chapter 15, How Models Work
Testing LLM Training Data and AI Pollution
The model learns from the data it eats, including bad data, stale data, biased data, and increasingly AI-generated data.
benchmarksynthetic datallm training data pollution
What to do
- Define runnable checks that exercise benchmark, synthetic data, and llm training data pollution.
- Set acceptable outcomes and blocker failures for benchmark, synthetic data, and llm training data pollution before running the evaluation.
- Run representative cases for benchmark, synthetic data, and llm training data pollution and preserve the failures that would change the decision.
Evidence to preserve
- Preserve the inputs, versions, configurations, raw outcomes, and results for benchmark, synthetic data, llm training data pollution needed to reproduce work on Testing LLM Training Data and AI Pollution.
- Report results for benchmark, synthetic data, llm training data pollution by relevant slice, separate blocker failures from averages, state uncertainty and blind spots, and connect the result to a release decision.
Expert note
In production work, test training-data risk through provenance audits, data cards, contamination checks, deduplication reports, benchmark-leakage probes, memorization tests, synthetic-data ratio tracking, and downstream slice evals. For closed models, treat these as vendor-risk questions and product-level stress tests.
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Cite this page
Jason Arbon. "Testing LLM Training Data and AI Pollution." Testing AI Knowledge Edition, section 114.
https://jarbon.ai/testing-ai/knowledge/ch114-llm-training-data-pollution.html