A Greek Government Decisions Dataset for Public-Sector Analysis and Insight

TL;DR

This paper presents a large-scale, high-quality Greek government decisions dataset, along with analysis and a retrieval-augmented generation system, to enhance public-sector transparency, information access, and support for language models in governmental domains.

Contribution

It introduces a comprehensive, machine-readable corpus of Greek government decisions, a reproducible extraction pipeline, and evaluates a RAG system for question answering over public decisions.

Findings

The dataset contains 1 million decisions with high-quality raw text.

A baseline RAG system effectively retrieves and reasons over government documents.

The corpus supports training and fine-tuning language models for legal and governmental tasks.

Abstract

We introduce an open, machine-readable corpus of Greek government decisions sourced from the national transparency platform Diavgeia. The resource comprises 1 million decisions, featuring and high-quality raw text extracted from PDFs. It is released with raw extracted text in Markdown format, alongside a fully reproducible extraction pipeline. Beyond the core dataset, we conduct qualitative analyses to explore boilerplate patterns and design a retrieval-augmented generation (RAG) task by formulating a set of representative questions, creating high-quality answers, and evaluating a baseline RAG system on its ability to retrieve and reason over public decisions. This evaluation demonstrates the potential of large-scale public-sector corpora to support advanced information access and transparency through structured retrieval and reasoning over governmental documents, and highlights how such a RAG pipeline could simulate a chat-based assistant capable of interactively answering questions about public decisions. Due to its scale, quality, and domain coverage, the corpus can also serve as high-value pre-training or fine-tuning material for new Language Models (LMs) and Large Language Models (LLMs) respectively, including specialized models for legal and governmental domains, and as a foundation for novel approaches in domain adaptation, knowledge-grounded generation, and explainable AI. Finally, we discuss limitations, outline future directions, and make both the data and the code accessible.