# kaal:claim:4855607-009

**Claim.** 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.

**Type.** failure  **Support.** evidenced

**Holds when.**

- legal applications of transformer based language models

**Source quote.**

> 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.

**From.** Wulf A. Kaal, *How AI Models are Optimized Through Web3 Governance* (2024), Model Overview: Transformer AI, page 21

**Cite as.** Wulf A. Kaal, How AI Models are Optimized Through Web3 Governance (2024). SSRN: https://ssrn.com/abstract=4855607

**Verify.** sha256 of source PDF `eb0b3e62374b45a8fa888c6bde9725e606bcb46cf4b5e74a6e851d9f25099113` at https://raw.githubusercontent.com/wulfkaal/Academic-Papers/main/papers/pdf/Kaal%20-%202024%20-%20How%20AI%20Models%20are%20Optimized%20Through%20Web3%20Governance.pdf

**Failure mode.** Restricted legal dataset access  (family: ai-model-and-training-failure)

**Topics.** ai-and-agents, law-and-legal-systems, empirical-evidence

**Keywords.** legal-ai, transformer-ai, dataset-access, adoption-barriers, resource-intensity

**Canonical form.** This markdown file is the canonical hashed representation of the claim. Its sha256 is the content hash used for attestation.