LORA: LOW-RANK ADAPTATION OF LARGE LANGUAGE MODELS

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LORA: LOW-RANK ADAPTATION OF LARGE LANGUAGE MODELS

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As we pre-train larger models, full fine-tuning, which retrains all model parameters, becomes less feasible. Using GPT-3 175B as an example – deploying independent instances of fine-tuned models, each with 175B parameters, is prohibitively expensive.

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.

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Problem

The major downside of fine-tuning is that the new model contains as many parameters as in the original model.

Existing techniques often introduce inference latency (Houlsby et al., 2019; Rebuffi et al., 2017) by extending model depth or reduce the model’s usable sequence length (Li & Liang, 2021; Lester et al., 2021; Hambardzumyan et al., 2020; Liu et al., 2021) (Section 3). More importantly, these method often fail to match the fine-tuning baselines, posing a trade-off between efficiency and model quality.

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We see a noticeable increase in latency when using adapters, even with a very small bottleneck dimension.

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Approach

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We limit our study to only adapting the attention weights for downstream tasks and freeze the MLP modules (so they are not trained in downstream tasks) both for simplicity and parameter-efficiency.

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Table 6 shows that, surprisingly, LoRA already performs competitively with a very small r (more so for { $W_{q}$, $W_{v}$ } than just $W_{q}$).