Summary of When Scaling Meets Llm Finetuning: the Effect Of Data, Model and Finetuning Method, by Biao Zhang et al.
When Scaling Meets LLM Finetuning: The Effect of Data, Model and Finetuning Method
by Biao Zhang, Zhongtao Liu, Colin Cherry, Orhan Firat
First submitted to arxiv on: 27 Feb 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 the inductive biases of large language models (LLMs) during finetuning for downstream applications. The authors conduct systematic experiments to study how different scaling factors, including model size, pretraining data size, and finetuning parameters, affect finetuning performance. They explore two types of finetuning: full-model tuning (FMT) and parameter-efficient tuning (PET), focusing on bilingual machine translation and multilingual summarization benchmarks. The results show that LLM finetuning follows a power-based multiplicative joint scaling law, with benefits from model scaling outweighing pretraining data scaling. PET is generally ineffective, and the optimal finetuning method depends on the task and finetuning data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to make large language models better for specific tasks. It tests different ways of adjusting these models to fit new jobs. The researchers use two types of adjustments: full-model tuning and parameter-efficient tuning. They test these methods on tasks like translating languages and summarizing texts. The results show that the best way to adjust a model depends on what task you’re trying to do. |
Keywords
* Artificial intelligence * Parameter efficient * Pretraining * Summarization * Translation
