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GPT Political Compass

by Community

Google Colab

GPT Political Compass screenshot

OSS

GPT Political Compass

Added 1 June 2026

Overview

A Google Colab notebook that evaluates the political orientation of language models by generating responses to a standardized set of questions and plotting them on a two-axis political compass chart. It is designed for quick, reproducible analysis without local setup.

Best for

Best for
Developers and researchers who want a quick, reproducible way to compare political bias across GPT models.

Use cases

  • Assessing the political bias of different GPT model versions
  • Comparing outputs of fine-tuned vs base models on political dimensions
  • Demonstrating or teaching model evaluation in a controlled Colab environment

Notes

A Google Colab notebook that evaluates the political orientation of language models by generating responses to a standardized set of questions and plotting them on a two-axis political compass chart. It is designed for quick, reproducible analysis without local setup.

Use cases

  • Assessing the political bias of different GPT model versions
  • Comparing outputs of fine-tuned vs base models on political dimensions
  • Demonstrating or teaching model evaluation in a controlled Colab environment

Pros

  • No local installation required, runs entirely in the browser via Google Colab
  • Quickly produces a visual political compass for easy interpretation
  • Free to use with a Google account

Cons

  • Relies on Colab’s resource limits, which may constrain larger models
  • Results depend heavily on prompt wording; no built-in mitigation for prompt sensitivity
  • Not a production-grade tool, intended more for exploration and education

Indexed from awesome-langchain and enriched against its public facts.

Pros

  • No local installation required, runs entirely in the browser via Google Colab
  • Quickly produces a visual political compass for easy interpretation
  • Free to use with a Google account

Cons

  • Relies on Colab's resource limits, which may constrain larger models
  • Results depend heavily on prompt wording; no built-in mitigation for prompt sensitivity
  • Not a production-grade tool, intended more for exploration and education
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