Summary of Take the Bull by the Horns: Hard Sample-reweighted Continual Training Improves Llm Generalization, By Xuxi Chen et al.
Take the Bull by the Horns: Hard Sample-Reweighted Continual Training Improves LLM Generalization
by Xuxi Chen, Zhendong Wang, Daouda Sow, Junjie Yang, Tianlong Chen, Yingbin Liang, Mingyuan Zhou, Zhangyang Wang
First submitted to arxiv on: 22 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 study addresses a critical challenge in large language models (LLMs) by developing a strategy for continual training using original pre-training data sets. The approach focuses on retaining samples that incur moderately high losses, deemed informative and beneficial for model refinement. This selective retention is formalized into an Instance-Reweighted Distributionally Robust Optimization (IR-DRO) framework, which dynamically prioritizes training focus on informative samples through instance reweighting. Experimental results show significant improvements in LLM performance across multiple benchmarks, including continual pre-training and instruction tuning scenarios. The study’s codes are available at this GitHub repository. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps improve large language models by finding a way to train them using the same data they were originally trained on. The idea is to keep some of the samples that make the model learn, but get ridged or noisy, and discard others that are too difficult for the model to learn from. This approach makes the model better at learning new things. The study shows that this method works well in many different situations and can be used with various models and data sets. |
Keywords
* Artificial intelligence * Instruction tuning * Optimization
