  Adapting Large Language Models (LLMs) to new tasks through fine-tuning has
been made more efficient by the introduction of Parameter-Efficient Fine-Tuning
(PEFT) techniques, such as LoRA. However, these methods often underperform
compared to full fine-tuning, particularly in scenarios involving complex
datasets. This issue becomes even more pronounced in complex domains,
highlighting the need for improved PEFT approaches that can achieve better
performance. Through a series of experiments, we have uncovered two critical
insights that shed light on the training and parameter inefficiency of LoRA.
Building on these insights, we have developed HydraLoRA, a LoRA framework with
an asymmetric structure that eliminates the need for domain expertise. Our
experiments demonstrate that HydraLoRA outperforms other PEFT approaches, even
those that rely on domain knowledge during the training and inference phases.
