Summary of Mitigating Training Imbalance in Llm Fine-tuning Via Selective Parameter Merging, by Yiming Ju et al.
Mitigating Training Imbalance in LLM Fine-Tuning via Selective Parameter Merging
by Yiming Ju, Ziyi Ni, Xingrun Xing, Zhixiong Zeng, hanyu Zhao, Siqi Fan, Zheng Zhang
First submitted to arxiv on: 1 Oct 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 Supervised fine-tuning (SFT) is a crucial technique for adapting Large Language Models (LLMs) to specific tasks. However, researchers have discovered that the order in which training data is presented can lead to significant imbalances, potentially degrading performance. To address this issue, the authors propose merging SFT models fine-tuned with different data orders, thereby enhancing the overall effectiveness of SFT. Moreover, they introduce a novel technique called “parameter-selection merging,” which outperforms traditional weighted-average methods on five benchmark datasets. The paper also includes analysis and ablation studies to validate the effectiveness of their approach and identify key factors contributing to performance improvements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine training a super smart AI model, but it’s only good at doing certain tasks because it was trained in a specific way. What if you could make it better by combining different ways of training? That’s what this research paper is all about! The authors found that the order in which they presented the training data mattered, and it could actually make the model worse. So, they came up with a new way to combine models trained with different orders, making them work even better together. They tested their method on five different datasets and showed that it outperformed other methods. |
Keywords
» Artificial intelligence » Fine tuning » Supervised
