标题： 特定领域大型语言模型的最大幻觉标准 显示英文标题

标题： Maximum Hallucination Standards for Domain-Specific Large Language Models

摘要： 大型语言模型（LLMs）常常生成不准确但听起来可信的内容，这被称为幻觉。 这种LLMs的固有特性带来了重大风险，尤其是在关键领域。 我将LLMs视为一类新的工程产品，将幻觉视为产品属性。 我证明了在LLMs幻觉和错误信息外部性认知不完善的情况下，当LLMs幻觉的最大可接受水平根据两个领域特定因素变化时，净福利会提高：对减少LLMs幻觉的支付意愿以及与错误信息相关的边际损害。 显示英文摘要

摘要： 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.