Summary of Parameter Efficient Instruction Tuning: An Empirical Study, by Pengfei He
Parameter Efficient Instruction Tuning: An Empirical Study
by Pengfei He
First submitted to arxiv on: 25 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The study investigates the impact of various hyperparameters on Parameter Efficient Finetuning (PEFT) methods for instruction tuning. It compares the performance of different PEFT methods, including LoRA and adapters, and explores how model size, number of instruction tasks, and training settings affect their capabilities. The findings suggest that LoRA and adapters can achieve similar performance to full finetuning under ideal conditions, but are prone to training instability if these conditions are not met. Additionally, LoRA requires a greater number of tasks for effective generalization and exhibits slower learning speed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how to make language models better at following instructions by fine-tuning them on specific tasks. It tries out different ways to do this efficiently, such as using smaller models or adapting existing ones. The results show that some methods can get close to the best possible performance if done just right, but they’re not perfect and have limitations. For example, one method called LoRA works well but needs more data to generalize to new tasks, while another method is better at certain types of reasoning but struggles with complex tasks. |
Keywords
» Artificial intelligence » Fine tuning » Generalization » Instruction tuning » Lora » Parameter efficient
