Generative AI in public administration: evaluating a fine-tuned large language model for policy briefing notes

Abstract

Abstract Recent literature shows that whilst Generative Artificial Intelligence may not be the cream of the crop for policy analysis, its potential for specific writing and synthesis tasks is increasingly evident. Large Language Models (LLMs) have demonstrated notable competencies for policy work in the fields of crafting policy and decoding complex legislation. In this article, we leveraged model fine-tuning to customize a base LLM for policy briefing notes in the Canadian context. We answer the question: Can fine-tuning a base model with past policy data make it better than a general-purpose foundation model? The study was designed with fine-tuning capabilities of the Python programming language and Google’s compute resources to train a policy-specialized model that was deployed and tested by human evaluators. Results suggest this could be a non-negligeable technique for public organizations looking for models capable of producing specialized policy briefing notes.

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