Summary of Scalable Fine-tuning From Multiple Data Sources: a First-order Approximation Approach, by Dongyue Li et al.
Scalable Fine-tuning from Multiple Data Sources: A First-Order Approximation Approach
by Dongyue Li, Ziniu Zhang, Lu Wang, Hongyang R. Zhang
First submitted to arxiv on: 28 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 paper introduces a novel algorithm for fine-tuning language models (LMs) on a target task by optimally using information from multiple auxiliary tasks. This problem has significant applications in natural language processing (NLP), such as targeted instruction tuning and data selection in chain-of-thought fine-tuning. The challenge lies in selecting the most useful subset of auxiliary tasks, as not all tasks are equally effective in improving performance. To address this issue, the authors propose a new algorithm that estimates model fine-tuning performances without repeated training. This approach involves multitask training using data from all tasks to obtain a meta initialization, followed by approximating the fine-tuning loss of a subset using functional values and gradients from the meta initialization. The authors demonstrate the effectiveness of this algorithm on twelve transformer-based LMs, achieving remarkable accuracy and a speedup of 30 times over conventional subset selection methods while incurring only 1% error. The paper also conducts extensive experiments to validate its approach, showing improvements over prior gradient or representation similarity-based methods for subset selection by up to 3.8%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how to fine-tune language models for a specific task using information from multiple other tasks. This is important because it can help us create better tools for understanding and generating human-like language. The problem is that not all of these auxiliary tasks are useful, so we need to figure out which ones to use. The authors develop a new way to do this without having to retrain the model on each task individually. They show that their approach works well for twelve different types of language models and can save a lot of time compared to traditional methods. |
Keywords
» Artificial intelligence » Fine tuning » Instruction tuning » Natural language processing » Nlp » Transformer
