Harsh Anand

Parameter-Efficient Fine-Tuning of Transformer Models: An Empirical Study of LoRA Optimization for High-Cardinality Intent Classification

Although parameter-efficient fine-tuning significantly reduces the computing cost of deep models, default configurations are insufficient to perform as good as full fine-tuning for challenging large-cardinality intent detection problems with 77-151 intents. Thus, this work presents the S1 configuration that is proposed...