Detect hallucinations using specialised Ollama model bespoke-minicheck

Description

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
Ensure the "bespoke-minicheck" model is installed in Ollama (ollama pull bespoke-minicheck)
Prepare a list of verified facts
Enter the text to be checked
Start the workflow
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.