Summary of Boosting Llm Via Learning From Data Iteratively and Selectively, by Qi Jia et al.
Boosting LLM via Learning from Data Iteratively and Selectively
by Qi Jia, Siyu Ren, Ziheng Qin, Fuzhao Xue, Jinjie Ni, Yang You
First submitted to arxiv on: 23 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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 proposed approach, IterIT, tackles the issue of instruction tuning by iteratively selecting and updating samples based on their complexity and diversity. Unlike traditional methods that calculate complexity scores once, IterIT updates these scores during fine-tuning to accommodate dynamic changes in the model. The approach also incorporates a diversity score defined by informativeness, allowing it to selectively prioritize complex and diverse responses. Experiments demonstrate consistent improvements over strong baselines on multiple instruction-tuning datasets, with generalizability to domain-specific scenarios and different backbone models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper explores ways to improve the quality of training data for AI models. The authors developed a new approach called IterIT that helps select the most useful samples from large datasets. They did this by considering how complex and diverse each sample is, and then updating these scores as they fine-tune their model. This means that IterIT can adapt to changing patterns in the data as it learns. The results show that IterIT outperforms previous methods on several different tasks. |
Keywords
» Artificial intelligence » Fine tuning » Instruction tuning
