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Maximum Hallucination Standards for Domain-Specific Large Language Models
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Large language models (LLMs) often generate inaccurate yet credible-sounding content, known as hallucinations. This inherent feature of LLMs poses significant risks, especially in critical domains. I analyze LLMs as a new class of engineering products, treating hallucinations as a product attribute. I demonstrate that, in the presence of imperfect awareness of LLM hallucinations and misinformation externalities, net welfare improves when the maximum acceptable level of LLM hallucinations is designed to vary with two domain-specific factors: the willingness to pay for reduced LLM hallucinations and the marginal damage associated with misinformation.
Forward citations
Cited by 2 Pith papers
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Beyond Misinformation: A Conceptual Framework for Studying AI Hallucinations in (Science) Communication
The paper proposes treating AI hallucinations as a distinct category of misinformation and outlines a research agenda for communication scholars.
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A comprehensive taxonomy of hallucinations in Large Language Models
A survey that organizes LLM hallucination types, causes, benchmarks, and mitigations, and restates the theorem that hallucination is inevitable for computable LLMs.
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