  Finetuning Large Language Models helps improve the results for
domain-specific use cases. End-to-end finetuning of large language models is
time and resource intensive and has high storage requirements to store the
finetuned version of the large language model. Parameter Efficient Fine Tuning
(PEFT) methods address the time and resource challenges by keeping the large
language model as a fixed base and add additional layers, which the PEFT
methods finetune. This paper demonstrates the evaluation results for one such
PEFT method Low Rank Adaptation (LoRA), for Clinical Dialogue Summarization.
The evaluation results show that LoRA works at par with end-to-end finetuning
for a large language model. The paper presents the evaluations done for solving
both the Subtask A and B from ImageCLEFmedical
{https://www.imageclef.org/2023/medical}
