Generative AI with LLMs: LoRA: A Parameter-Efficient Fine-Tuning Method for LLMs

Learn how LoRA works and why it is a powerful technique for fine-tuning large language models (LLMs) for reading comprehension and generation tasks.

Question

Which of the following best describes how LoRA works?

A. LoRA trains a smaller, distilled version of the pre-trained LLM to reduce model size
B. LoRA decomposes weights into two smaller rank matrices and trains those instead of the full model weights.
C. LoRA freezes all weights in the original model layers and introduces new components which are trained on new data.
D. LoRA continues the original pre-training objective on new data to update the weights of the original model.

Answer

C. LoRA freezes all weights in the original model layers and introduces new components which are trained on new data.

Explanation

The correct answer is C. LoRA freezes all weights in the original model layers and introduces new components which are trained on new data. LoRA stands for Layer-wise Optimization of large language models for Reading comprehension and generation Applications. It is a parameter-efficient fine-tuning method that preserves the pre-trained knowledge of large language models (LLMs) while adapting them to new tasks or domains. LoRA inserts new components, such as adapters, into the original model layers and only trains those components, leaving the rest of the model fixed. This reduces the number of trainable parameters and the risk of overfitting or catastrophic forgetting. LoRA also applies layer-wise optimization techniques, such as layer dropping and layer reordering, to further improve the performance and efficiency of fine-tuning.

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