LoRA: Low-Rank Adaptation of Large Language Models

An important paradigm of natural language processing consists of large-scale pre-training on general domain data and adaptation to particular tasks or domains. We also provide an empirical investigation into rank-deficiency in language model adaptation, which sheds light on the efficacy of LoRA.

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• 1 As we pre-train larger models, full fine-tuning, which retrains all model parameters, becomes less feasible.
• 2 Using GPT-3 175B as an example -- deploying independent instances of fine-tuned models, each with 175B parameters, is prohibitively expensive.
• 3 We propose Low-Rank Adaptation, or LoRA, which freezes the pre-trained model weights and injects trainable rank decomposition matrices into each layer of the Transformer architecture, greatly reducing the number of trainable parameters for downstream tasks.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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