Low-rank Adaptation of Large Language Model Rescoring for Parameter-Efficient Speech Recognition

However, as the size of the pretrained models increases, the cost associated We propose a neural language modeling system based on with fine-tuning and deploying these models for low-rank adaptation (LoRA) for speech recognition output real-world applications also escalates. To address this practical rescoring. Although pretrained language models (LMs) challenge, a range of parameter-efficient methods (e.g., like BERT have shown superior performance in second-pass adapters, model reprogramming, and prompts) have been proposed rescoring, the high computational cost of scaling up the pretraining [11, 12, 13, 14, 15, 16, 17, 18] to alleviate the computation stage and adapting the pretrained models to specific and memory demands of fine-tuning LLMs. Low-rank domains limit their practical use in rescoring. Here we present adaptation (LoRA) [19] freezes all pretrained parameters in a method based on low-rank decomposition to train a rescoring the LLM and inserts a trainable pair of matrices (acting as a BERT model and adapt it to new domains using only a low-rank decomposition of a full matrix) additively into each fraction (0.08%) of the pretrained parameters. These inserted layer of the Transformer architecture. Compared to other matrices are optimized through a discriminative training objective parameter-efficient training methods, such as adapters [12], along with a correlation-based regularization loss. The LoRA has two distinct advantages: 1) it employs a simple proposed low-rank adaptation RescoreBERT (LoRB) architecture architecture and has the potential to reduce the number of is evaluated on LibriSpeech and internal datasets with trainable parameters compared to alternatives; 2) LoRA does decreased training times by factors between 5.4 and 3.6.