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Scaling a language model is not only a question of final quality: the architectural choice determines how much computation is spent during training, prompt processing, and autoregressive decoding to achieve certain model quality. An ideal model architecture should lower all above computation costs to facilitate scaling to a larger model, while ensure the larger model indeed outperforms smaller baselines.
Paper identifies and investigates critical factors affecting single DNA tube capacity for archival storage, finally, authors suggest several promising directions to overcome limitations and enhance DNA storage capacity.
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