Fact-Checking Workflow Documentation

Overview

This workflow is designed for automated fact-checking of texts. It uses AI models to compare a given text with a list of facts and identify potential discrepancies or hallucinations.

Components

1. Input

• The workflow can be initiated in two ways:
  a) Manually via the "When clicking 'Test workflow'" trigger
  b) By calling from another workflow via the "When Executed by Another Workflow" trigger
• Required inputs:
  • facts: A list of verified facts
  • text: The text to be checked

2. Text Preparation

• The "Code" node splits the input text into individual sentences
• Takes into account date specifications and list elements

3. Fact Checking

• Each sentence is individually compared with the given facts
• Uses the "bespoke-minicheck" Ollama model for verification
• The model responds with "Yes" or "No" for each sentence

4. Filtering and Aggregation

• Sentences marked as "No" (not fact-based) are filtered
• The filtered results are aggregated

5. Summary

• A larger language model (Qwen2.5) creates a summary of the results
• The summary contains:
  • Number of incorrect factual statements
  • List of incorrect statements
  • Final assessment of the article's accuracy

Usage

1. Ensure the "bespoke-minicheck" model is installed in Ollama (ollama pull bespoke-minicheck)
2. Prepare a list of verified facts
3. Enter the text to be checked
4. Start the workflow
5. The results are output as a structured summary

Notes

• The workflow ignores small talk and focuses on verifiable factual statements
• Accuracy depends on the quality of the provided facts and the performance of the AI models

Customization Options

• The summarization function can be adjusted or removed to return only the raw data of the issues found
• The AI models used can be exchanged if needed

This workflow provides an efficient method for automated fact-checking and can be easily integrated into larger systems or editorial workflows.