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 "name": "Legal ai",
 "termCode": "legal-ai",
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 "author": {
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  "name": "Wulf A. Kaal",
  "identifier": "https://orcid.org/0000-0003-0757-275X"
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   "@id": "https://wulfkaal.github.io/claims/4855607-009",
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   "text": "In the legal domain the adoption of transformer based language models is blocked less by capability than by resources and access: training and deployment are resource intensive and large, quality tagged legal datasets are usually restricted.",
   "abstract": "In the legal domain, training and deploying Transformer-based Language Models (TLMs) is resource-intensive, and access to large, quality-tagged legal datasets is often restricted, hindering widespread adoption.",
   "citation": "Wulf A. Kaal, How AI Models are Optimized Through Web3 Governance (2024). SSRN: https://ssrn.com/abstract=4855607",
   "datePublished": "2024",
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   "@id": "https://wulfkaal.github.io/claims/6421319-022",
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   "text": "Hallucination rates vary sharply by domain: leading frontier models achieve sub one percent rates for general knowledge queries, while rates climb to five to thirty percent for specialized domains and legal information hallucination averages 6.4 percent even for top models.",
   "abstract": "While leading probabilistic frontier models have achieved sub-1% hallucination rates for general knowledge queries, rates climb to 5–30% for specialized domains, and legal information hallucination rates average 6.4% even for top models.",
   "citation": "Wulf A. Kaal, The Collapse of Scarcity Economics (2026). SSRN: https://ssrn.com/abstract=6421319",
   "datePublished": "2026",
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