To assess whether the content generated by a Generative AI is truthful and faithful, several methods and frameworks can be employed. Truthfulness refers to whether the generated content is factually correct, while faithfulness refers to whether it accurately reflects the input data or prompt.
1. Frameworks for Truthfulness and Faithfulness
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Subject Matter Expert (SME) Reviews: One of the most reliable methods for verifying truthfulness and faithfulness is through SME validation. SMEs can manually check the content to ensure it aligns with domain-specific knowledge and is factually accurate.
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Knowledge Graph and External Data: Generative AI models can be linked to external sources of truth, such as knowledge graphs, databases, or other verified resources. This allows the system to cross-check facts and improve the truthfulness of the content.
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Retrieval-Augmented Generation (RAG): This framework involves retrieving relevant information from trusted sources before generating content. It helps ensure that the AI is providing up-to-date, reliable, and contextually accurate responses.
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Evaluation Metrics: Some metrics can be used to evaluate faithfulness:
- Factual Consistency Metrics: Tools such as BERTScore or FactCC can compare generated text with reference text or factual databases to check for consistency.
- Human Evaluation: In certain contexts, human evaluators rate the content on aspects of truthfulness and faithfulness. This can be part of quality assurance processes.
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Cross-Referencing Data: AI-generated content should be cross-referenced with existing, credible sources to confirm its accuracy. For example, if the AI makes a historical claim or provides statistical data, those facts should be verifiable through known data repositories.
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Fact-Checking Tools: Using automated fact-checking tools or models trained to detect false information can provide another layer of defence against untruthful content.