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   "@id": "https://wulfkaal.github.io/claims/4855607-007",
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   "text": "The self attention mechanism in transformers scales quadratically with input sequence length, which makes transformers expensive and slow to train and use on long sequences and disqualifies them where real time processing or limited compute is required.",
   "abstract": "The self-attention mechanism in Transformers has a quadratic computational complexity with respect to the input sequence length, making them computationally expensive and time-consuming to train and use, especially for long sequences.",
   "citation": "Wulf A. Kaal, How AI Models are Optimized Through Web3 Governance (2024). SSRN: https://ssrn.com/abstract=4855607",
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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",
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   "text": "The interaction between transformer models and the web3 community forms an evolutionary feedback loop in which community input shapes model development and the improved models return better service to the community, which is what sustains participation.",
   "abstract": "The interaction between Transformer models and the web3 community creates a symbiotic relationship where both evolve together. As the community inputs shape the development of the models, the improved models, in turn, offer better services or more accurate responses that benefit the community.",
   "citation": "Wulf A. Kaal, How AI Models are Optimized Through Web3 Governance (2024). SSRN: https://ssrn.com/abstract=4855607",
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