Fine-tuning is a process that adapts a pre-trained foundational model to a specific, narrower domain by continuing the training process on a smaller, curated, and high-quality dataset relevant to the organization. This technique "grounds" the model in the organization's specific context and factual data. By constraining the model's knowledge to this trusted information, it directly reduces the likelihood of it generating fabricated or nonsensical information (hallucinations) when answering user queries. It is the most direct technical control listed for mitigating this specific risk.

A. Performing model testing and validation is a detective control that identifies the presence and frequency of hallucinations but does not, by itself, mitigate or prevent them.
B. Training the foundational model on large data sets is what creates the base model. This broad, uncurated training is often a source of hallucinations, not a specific mitigation for a deployed application.
C. Ensuring model developers have been trained in AI risk is a procedural and preventative control. While important for governance, it does not directly alter the model's output or reduce hallucinations.

1. ISACA

Generative AI: A Practical Audit Approach

2023: In the section "Key Risks and Controls for Generative AI

" the risk of "Inaccurate or Fabricated Information (Hallucinations)" is discussed. A key control mentioned is to "Fine-tune with domain-specific data...to help ground the model in a specific context

reducing the likelihood of generating irrelevant or fabricated information." (Page 11

Table 1).

2. Stanford University

Center for Research on Foundation Models (CRFM)

On the Opportunities and Risks of Foundation Models

2021: The report explains that fine-tuning (referred to as adaptation) is a primary method for specializing a foundation model for downstream tasks. This specialization improves performance and alignment with desired behaviors

such as factuality. (Section 4.2

"Adaptation

" pp. 85-87).

3. Zhang

Y.

et al.

A Survey on Hallucination in Large Language Models: Principles

Taxonomy

and Mitigation

ACM Computing Surveys

2024: This peer-reviewed academic survey categorizes mitigation strategies. It places fine-tuning under "Model-based" mitigation techniques

describing it as a method to align the model's knowledge with factual data sources

thereby directly reducing knowledge-based hallucinations. (Section 5.2.2

"Supervised Fine-tuning"). DOI: https://doi.org/10.1145/3626428