Constitution LLM: An Open-Source Tool for Analyzing Constitutions of Ancient Regimes Using Large Language Models

Constitution LLM is an academic-grade open-source tool for political scientists, historians, and digital humanities researchers. It aims to address the low efficiency of traditional manual analysis of historical constitutional texts using large language models (LLMs). It supports multi-model validation, batch processing, and provides evaluation of 9 political indicators, offering quantitative data support for comparative political research.

Traditionally, historians have to manually read and analyze large volumes of historical constitutional texts, which is not only time-consuming and labor-intensive but also difficult to standardize. The core goal of Constitution LLM is to solve this problem: by introducing LLM technology and multi-model validation mechanisms, it helps researchers quickly obtain quantitative indicators of the political structure of ancient regimes, promoting the scaling of comparative political research.

Multi-LLM Support

The project is compatible with major providers such as OpenAI (GPT-5, etc.), Anthropic (Claude 4.5 Sonnet, etc.), Google Gemini (default), and AWS Bedrock, allowing researchers to balance cost and quality.

9 Political Indicators

Covers sovereign structure (sovereignty, federalism), checks and balances in decision-making (checks and balances capacity, collegial system, type of parliament), and leadership mechanisms (fine-grained classification of inauguration/leaving office methods), providing quantitative descriptions of the characteristics of ancient regimes.

Self-Consistency Validation

Call the model multiple times for the same question and vote, outputting validation results, consistency ratio, and uncertainty level to ensure stable results.

Chain of Validation (CoVe)

Check initial predictions through cross-model validation and 4 factual questions, use independent validation models to improve accuracy, and allow customizing validation models via configuration files.

Prompt Modes

• Single: Process all indicators at once (quick preview)
• Multiple: Prompt for each indicator separately (recommended for production environments)
• Sequential: Process indicators in order (study the impact of question order)

Search Modes

Supports three modes: None (default), Agentic (autonomous search), and Forced (mandatory search) to enhance the accuracy of historical facts.

Typical Scenarios

Provides examples of command-line batch processing, analysis with validation, and Python API calls to meet different research needs.

Academic Value

• Scaled processing: Significantly improve the efficiency of historical text analysis
• Cross-regime comparison: Unified indicator framework supports cross-temporal and cross-spatial research
• Methodological contribution: Explore the application boundaries of LLMs in the historical field

Limitations

• Historical knowledge accuracy: LLM training data may lack information on less-known regimes
• Annotation subjectivity: Political indicators have room for interpretation
• Cost considerations: API costs for large-scale analysis are relatively high

Constitution LLM is an innovative application of LLM technology in historical political science research, providing a complete toolchain to support constitutional analysis with multi-models, multi-indicators, and multi-validation strategies. Its design balances academic rigor and flexibility, opening up new paths for digital humanities research. With the advancement of LLM technology, such interdisciplinary tools will further promote the digital transformation of historical research.