IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2503.05481.html
   My bibliography  Save this paper

Maximum Hallucination Standards for Domain-Specific Large Language Models

Author

Listed:
  • Tingmingke Lu

Abstract

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.

Suggested Citation

  • Tingmingke Lu, 2025. "Maximum Hallucination Standards for Domain-Specific Large Language Models," Papers 2503.05481, arXiv.org.
    • Handle: RePEc:arx:papers:2503.05481
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2503.05481
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nicola Jones, 2025. "AI hallucinations can’t be stopped — but these techniques can limit their damage," Nature, Nature, vol. 637(8047), pages 778-780, January.
    2. Sebastien Houde & Erica Myers, 2019. "Heterogeneous (Mis-) Perceptions of Energy Costs: Implications for Measurement and Policy Design," CER-ETH Economics working paper series 19/314, CER-ETH - Center of Economic Research (CER-ETH) at ETH Zurich.
    3. Mathias Reynaert, 2021. "Abatement Strategies and the Cost of Environmental Regulation: Emission Standards on the European Car Market," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 88(1), pages 454-488.
    4. Kellogg, Ryan, 2018. "Gasoline price uncertainty and the design of fuel economy standards," Journal of Public Economics, Elsevier, vol. 160(C), pages 14-32.
    5. Koichiro Ito & James M. Sallee, 2018. "The Economics of Attribute-Based Regulation: Theory and Evidence from Fuel Economy Standards," The Review of Economics and Statistics, MIT Press, vol. 100(2), pages 319-336, May.
    6. Karan Singhal & Shekoofeh Azizi & Tao Tu & S. Sara Mahdavi & Jason Wei & Hyung Won Chung & Nathan Scales & Ajay Tanwani & Heather Cole-Lewis & Stephen Pfohl & Perry Payne & Martin Seneviratne & Paul G, 2023. "Publisher Correction: Large language models encode clinical knowledge," Nature, Nature, vol. 620(7973), pages 19-19, August.
    7. Mathias Reynaert & James M. Sallee, 2021. "Who Benefits When Firms Game Corrective Policies?," American Economic Journal: Economic Policy, American Economic Association, vol. 13(1), pages 372-412, February.
    8. Allcott, Hunt & Mullainathan, Sendhil & Taubinsky, Dmitry, 2014. "Energy policy with externalities and internalities," Journal of Public Economics, Elsevier, vol. 112(C), pages 72-88.
    9. Andreas Gerster & Michael Kramm, 2024. "Optimal Internality Taxation of Product Attributes," American Economic Journal: Economic Policy, American Economic Association, vol. 16(3), pages 394-419, August.
    10. Grégoire-Zawilski, Myriam & Popp, David, 2024. "Do technology standards induce innovation in environmental technologies when coordination is important?," Research Policy, Elsevier, vol. 53(1).
    11. Mariarosaria Comunale & Andrea Manera, 2024. "The Economic Impacts and the Regulation of AI: A Review of the Academic Literature and Policy Actions," IMF Working Papers 2024/065, International Monetary Fund.
    12. Alon Eizenberg, 2014. "Upstream Innovation and Product Variety in the U.S. Home PC Market," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 81(3), pages 1003-1045.
    13. Helen Pearson, 2025. "Are the Internet and AI affecting our memory? What the science says," Nature, Nature, vol. 638(8049), pages 26-28, February.
    14. Karan Singhal & Shekoofeh Azizi & Tao Tu & S. Sara Mahdavi & Jason Wei & Hyung Won Chung & Nathan Scales & Ajay Tanwani & Heather Cole-Lewis & Stephen Pfohl & Perry Payne & Martin Seneviratne & Paul G, 2023. "Large language models encode clinical knowledge," Nature, Nature, vol. 620(7972), pages 172-180, August.
    15. Rik L. Rozendaal & Herman R. J. Vollebergh & Rik Rozendaal, 2021. "Policy-Induced Innovation in Clean Technologies: Evidence from the Car Market," CESifo Working Paper Series 9422, CESifo.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Rik L. Rozendaal & Herman R. J. Vollebergh & Rik Rozendaal, 2021. "Policy-Induced Innovation in Clean Technologies: Evidence from the Car Market," CESifo Working Paper Series 9422, CESifo.
    2. Lu, Tingmingke, 2023. "Response of new car buyers to alternative energy policies: The role of vehicle use heterogeneity," Economic Modelling, Elsevier, vol. 120(C).
    3. Yujie Lin & Joshua Linn, 2023. "Environmental Regulation and Product Attributes: The Case of European Passenger Vehicle Greenhouse Gas Emissions Standards," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 10(1), pages 1-32.
    4. Mathias Reynaert, 2021. "Abatement Strategies and the Cost of Environmental Regulation: Emission Standards on the European Car Market," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 88(1), pages 454-488.
    5. Soren T. Anderson & James M. Sallee, 2016. "Designing Policies to Make Cars Greener: A Review of the Literature," NBER Working Papers 22242, National Bureau of Economic Research, Inc.
    6. Maxime Griot & Coralie Hemptinne & Jean Vanderdonckt & Demet Yuksel, 2025. "Large Language Models lack essential metacognition for reliable medical reasoning," Nature Communications, Nature, vol. 16(1), pages 1-10, December.
    7. Ching-Nam Hang & Pei-Duo Yu & Roberto Morabito & Chee-Wei Tan, 2024. "Large Language Models Meet Next-Generation Networking Technologies: A Review," Future Internet, MDPI, vol. 16(10), pages 1-29, October.
    8. Ale-Chilet, Jorge & Chen, Cuicui & Li, Jing & Reynaert, Mathias, 2021. "Colluding Against Environmental Regulation," TSE Working Papers 21-1204, Toulouse School of Economics (TSE).
    9. Konishi, Yoshifumi & Kuroda, Sho, 2023. "Why is Japan’s carbon emissions from road transportation declining?," Japan and the World Economy, Elsevier, vol. 66(C).
    10. Antonio M. Bento & Mark R. Jacobsen & Christopher R. Knittel & Arthur A. van Benthem, 2020. "Estimating the Costs and Benefits of Fuel-Economy Standards," Environmental and Energy Policy and the Economy, University of Chicago Press, vol. 1(1), pages 129-157.
    11. Ali Nemati & Mohammad Assadi Shalmani & Qiang Lu & Jake Luo, 2025. "Benchmarking Large Language Models from Open and Closed Source Models to Apply Data Annotation for Free-Text Criteria in Healthcare," Future Internet, MDPI, vol. 17(4), pages 1-27, March.
    12. Pavan, Giulia, 2017. "Green Car Adoption and the Supply of Alternative Fuels," TSE Working Papers 17-875, Toulouse School of Economics (TSE).
    13. Ofir Ben Shoham & Nadav Rappoport, 2024. "CPLLM: Clinical prediction with large language models," PLOS Digital Health, Public Library of Science, vol. 3(12), pages 1-15, December.
    14. Fournel, Jean-François, 2023. "Electric Vehicle Subsidies: Cost-Effectiveness and Emission Reductions," TSE Working Papers 23-1465, Toulouse School of Economics (TSE), revised Jun 2024.
    15. Myers, Erica & Souza, Mateus, 2020. "Social comparison nudges without monetary incentives: Evidence from home energy reports," Journal of Environmental Economics and Management, Elsevier, vol. 101(C).
    16. Venkat Ram Reddy Ganuthula & Krishna Kumar Balaraman, 2025. "The Paradox of Professional Input: How Expert Collaboration with AI Systems Shapes Their Future Value," Papers 2504.12654, arXiv.org.
    17. Houde, Sebastien & Aldy, Joseph E., 2017. "The Efficiency Consequences of Heterogeneous Behavioral Responses to Energy Fiscal Policies," Working Paper Series rwp17-047, Harvard University, John F. Kennedy School of Government.
    18. Cheng-Yi Li & Kao-Jung Chang & Cheng-Fu Yang & Hsin-Yu Wu & Wenting Chen & Hritik Bansal & Ling Chen & Yi-Ping Yang & Yu-Chun Chen & Shih-Pin Chen & Shih-Jen Chen & Jiing-Feng Lirng & Kai-Wei Chang & , 2025. "Towards a holistic framework for multimodal LLM in 3D brain CT radiology report generation," Nature Communications, Nature, vol. 16(1), pages 1-14, December.
    19. Cattaneo, Cristina, 2018. "Internal and External Barriers to Energy Efficiency: Made-to-Measure Policy Interventions," CSI: Climate and Sustainable Innovation 269536, Fondazione Eni Enrico Mattei (FEEM).
    20. Kevin Wu & Eric Wu & Kevin Wei & Angela Zhang & Allison Casasola & Teresa Nguyen & Sith Riantawan & Patricia Shi & Daniel Ho & James Zou, 2025. "An automated framework for assessing how well LLMs cite relevant medical references," Nature Communications, Nature, vol. 16(1), pages 1-10, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2503.05481. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.