AIAdvanced#Machine Learning#Evaluation#Data Governance

Fine-Tuning

Definition

Additional training of an existing model on a selected dataset for a defined task. It may change model behavior on evaluated examples; it does not prove domain accuracy, reduce factual errors by itself, or create a reliable trading system.

Source reviewed

NIST: Adversarial Machine Learning Taxonomy opens in a new tab
Supports: NIST documents data and model poisoning risks across predictive and generative AI systems, including training and fine-tuning stages.
NIST AI RMF: Generative AI Profile opens in a new tab
Supports: NIST explains that generative systems can still produce confidently false or inconsistent content, especially consequential when people act on it.

Concept Diagram

Key Takeaways

Fine-tuning changes an existing model with a selected dataset for a stated task; it does not prove domain expertise or reliable market conclusions.

Record provenance, permission, time range, labels, transformations, privacy rules, gaps, and approval for the training data.

Evaluate against time-aware held-out data and known failure cases, reporting metrics, error categories, date window, and limitations.

Keep fine-tuned output as reviewable assistance and put verification, transaction policy, and execution behind independent controls.

Practical Example

A team fine-tunes a classifier to label whether a supplied official document concerns a protocol upgrade, incident, or governance vote. It records source permissions, date splits, labels, test errors, and a fallback for low-confidence results. The output routes a document to review; it never creates a market signal or transaction.

Finished learning?

Related Terms

LLM Reasoning

Using a language model to summarize, classify, or compare supplied information. Its output is probabilistic text, not verified market analysis, financial advice, or authority to execute a trade.

Model Collapse

Degradation that can occur when generative models are repeatedly trained on their own or other model-generated outputs, especially when the training process loses rare but important parts of the original data distribution.

Prompt Engineering

The practice of designing model instructions, examples, context, and output constraints for a bounded task. It can make a workflow easier to evaluate; it does not make model output deterministic, correct, secure, or suitable for a financial decision.

Inference

The stage where a deployed model processes an input and produces an output. An inference result can be a label, score, generated text, or structured draft; it does not verify its own input, predict a market reliably, or authorize an action.

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