Fact-Filtering Frameworks

Abstract

The ubiquitous nature of Large Language Models (LLMs) into health, law, and education has intensified worries regarding the validity of facts and the creation of hallucinations. The chapter outlines a systematic discussion of the verification frameworks as downstream constraints in fact verification mechanisms to make generative AI systems more truthful and reliable. The chapter subsequently suggests a multi-layered fact-filtering structure, which incorporates knowledge graph validation, semantic entailment, and cross-source agreement strategies of real time fact validation. Benchmark data, measurement criteria and execution processes are addressed in order to evaluate performance trade-offs. Combining theoretical grounding, realistic design practices, and supporting analysis, this chapter will act as a guide towards the creation of hallucination-resistant LLMs which will enable veridical and accountable and aligned AI applications in the high stakes decision-making contexts.

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