Summary of Comparative Analysis Of Different Efficient Fine Tuning Methods Of Large Language Models (llms) in Low-resource Setting, by Krishna Prasad Varadarajan Srinivasan et al.
Comparative Analysis of Different Efficient Fine Tuning Methods of Large Language Models (LLMs) in Low-Resource Setting
by Krishna Prasad Varadarajan Srinivasan, Prasanth Gumpena, Madhusudhana Yattapu, Vishal H. Brahmbhatt
First submitted to arxiv on: 21 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 This paper investigates various fine-tuning strategies for large language models (LLMs) on out-of-domain datasets. The authors build upon previous work that demonstrated similar generalization performance among Vanilla Fine Tuning, Pattern-Based Fine Tuning, and In-Context Learning. However, they also highlight the challenges posed by these methods, particularly in terms of memory requirements. To better understand fine-tuning strategies, the authors conduct experiments using state-of-the-art methods like vanilla fine-tuning and Pattern-Based Fine-Tuning on pre-trained models across two datasets: COLA and MNLI. The study also explores adaptive fine-tuning, LoRA adapters in a few-shot setting, and context distillation as an alternative approach. By comparing these different strategies, the paper aims to provide a comprehensive understanding of full-model fine-tuning for LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how we can teach large language models to learn new things from small amounts of data. The scientists tested different ways of doing this and found that some methods work similarly well, but others have drawbacks like using a lot of memory. They experimented with different approaches on two datasets: one for news articles and another for conversations. By comparing these different methods, the researchers want to help us understand which ones are best for teaching large language models new skills. |
Keywords
» Artificial intelligence » Distillation » Few shot » Fine tuning » Generalization » Lora
