# Parameter Efficient Instruction Tuning: An Empirical Study

**Authors:** Pengfei He  
**arXiv:** [2411.16775](https://arxiv.org/abs/2411.16775)  
**Date:** 2024-11-25  
**Summary page:** https://www.summarizepaper.com/en/paper/2411.16775v1/

## Key points

- Parameter Efficient Finetuning (PEFT) is a cost-effective alternative to full parameter finetuning for pretrained language models.
- PEFT methods such as LoRA and adapter can closely approximate the performance of full finetuning under ideal training conditions.
- Both LoRA and adapter may experience training instability if not optimized properly.
- LoRA surpasses adapter in open instruction tuning settings by demonstrating robust generalization across diverse tasks but requires a substantial number of tasks for effective unseen task generalization.
- Limitations exist in long-form generation capabilities for both methods, highlighting ongoing challenges within PEFT approaches that require further innovation.
- Tailoring PEFT methods to specific model sizes, task types, and data availability is crucial for optimal performance.
- Continued refinement of PEFT methods is needed to enhance stability and expand applicability to more complex datasets in the evolving landscape of language model finetuning.
- Researchers should explore trade-offs between different PEFT strategies to advance towards more sophisticated techniques that maximize efficiency and effectiveness in instruction tuning scenarios across various domains.

## Summary

In the realm of instruction tuning for finetuning pretrained language models, Parameter Efficient Finetuning (PEFT) has emerged as a cost-effective alternative to full parameter finetuning. This is due to its significantly smaller computational, memory, and storage costs. This study delves into the effectiveness of various PEFT methods, with a particular focus on LoRA and adapter. The research showcases that these methods can closely approximate the performance of full finetuning when implemented under ideal training conditions. These conditions include appropriate learning rates and larger LoRA ranks or adapter sizes, along with diverse training tasks. However, it is noted that both LoRA and adapter may experience training instability if not optimized properly. Furthermore, while LoRA surpasses adapter in open instruction tuning settings by demonstrating robust generalization across diverse tasks, it requires a substantial number of tasks to achieve effective unseen task generalization. The study also highlights limitations in long-form generation capabilities for both methods, indicating ongoing challenges within PEFT approaches that necessitate further innovation and exploration. The findings underscore the importance of tailoring PEFT methods to specific model sizes, task types, and data availability for optimal performance. As the landscape of language model finetuning evolves, there is a call for continued refinement of these methods to enhance stability and expand applicability to more complex datasets. By exploring the trade-offs between different PEFT strategies and their impacts on model performance, researchers can advance towards more sophisticated techniques that maximize both efficiency and effectiveness in instruction tuning scenarios across various domains. Ultimately, this study aims to guide future research efforts towards optimizing the efficiency and efficacy of instruction tuning practices in real-world applications.